Approximate inference is key to modern probabilistic modeling. Thanks to the availability of big data, significant computational power, and sophisticated models, machine learning has achieved many breakthroughs in multiple application domains. At the same time, approximate inference becomes critical since exact inference is intractable for most models of interest. Within the field of approximate Bayesian inference, variational and Monte Carlo methods are currently the mainstay techniques. For both methods, there has been considerable progress both on the efficiency and performance.

In this workshop, we encourage submissions advancing approximate inference methods. We are open to a broad scope of methods within the field of Bayesian inference. In addition, we also encourage applications of approximate inference in many domains, such as computational biology, recommender systems, differential privacy, and industry applications.

Dear NIPS Workshop Chairs,

We propose to organize the workshop:

OPT 2017: Optimization for Machine Learning.

This year marks a major milestone in the history of OPT, as it will be the 10th anniversary edition of this long running NIPS workshop.

The previous OPT workshops enjoyed packed to overpacked attendance. This huge interest is no surprise: optimization is the 2nd largest topic at NIPS and is indeed foundational for the wider ML community.

Looking back over the past decade, a strong trend is apparent: The intersection of OPT and ML has grown monotonically to the point that now several cutting-edge advances in optimization arise from the ML community. The distinctive feature of optimization within ML is its departure from textbook approaches, in particular, by having a different set of goals driven by “big-data,” where both models and practical implementation are crucial.

This intimate relation between OPT and ML is the core theme of our workshop. OPT workshops have previously covered a variety of topics, such as frameworks for convex programs (D. Bertsekas), the intersection of ML and optimization, especially SVM training (S. Wright), large-scale learning via stochastic gradient methods and its tradeoffs (L. Bottou, N. Srebro), exploitation of structured sparsity (Vandenberghe), randomized methods for extremely large-scale convex optimization (A. Nemirovski), complexity theoretic foundations of convex optimization (Y. Nesterov), distributed large-scale optimization (S. Boyd), asynchronous and sparsity based stochastic gradient (B. Recht), algebraic techniques in machine learning (P. Parrilo), insights into nonconvex optimization (A. Lewis), sums-of-squares techniques (J. Lasserre), optimization in the context of deep learning (Y. Bengio), stochastic convex optimization (G. Lan), new views on interior point (E. Hazan), among others.

Several ideas propounded in these talks have become important research topics in ML and optimization --- especially in the field of randomized algorithms, stochastic gradient and variance reduced stochastic gradient methods. An edited book "Optimization for Machine Learning" (S. Sra, S. Nowozin, and S. Wright; MIT Press, 2011) grew out of the first three OPT workshops, and contains high-quality contributions from many of the speakers and attendees, and there have been sustained requests for the next edition of such a volume.

We wish to use OPT2017 as a platform to foster discussion, discovery, and dissemination of the state-of-the-art in optimization as relevant to machine learning. And even beyond that, as a platform to identify new directions and challenges that will drive future research.

Continuing its trend, the workshop will bring experts in optimization to share their perspectives while leveraging crossover experts in ML to share their views and recent advances. Our tentative invited speakers for this year are:

Yurii Nesterov (already agreed)
Dimitri Bertsekas (already agreed)
Francis Bach (already agreed)

Distinction from other optimization workshops at NIPS:

Compared to the other optimization focused workshops that happen (or have happened) at NIPS, key distinguishing features of OPT are: (a) it provides a unique bridge between the ML community and the wider optimization community, and is the longest running NIPS workshop on optimization (since NIPS 2008); (b) it encourages theoretical work on an equal footing with practical efficiency; and (c) it caters to a wide body of NIPS attendees, experts and beginners alike; (d) it covers optimization in a broad-spectrum, with a focus on bringing in new optimization ideas from different communities into ML while identifying key future directions for the broader OPTML community.

Organization
----------------

The main features of the proposed workshop are:

1. One day long with morning and afternoon sessions
2. Four invited talks by leading experts from optimization and ML
3. Contributed talks from the broader OPT and ML community
4. A panel discussion exploring key future research directions for OPTML.

Traditionally, machine learning has been focused on methods where objects reside in continuous domains. The goal of this workshop is to advance state-of-the-art methods in machine learning that involve discrete structures.

Models with ultimately discrete solutions play an important role in machine learning. At its core, statistical machine learning is concerned with making inferences from data, and when the underlying variables of the data are discrete, both the tasks of model inference as well as predictions using the inferred model are inherently discrete algorithmic problems. Many of these problems are notoriously hard, and even those that are theoretically tractable become intractable in practice with abundant and steadily increasing amounts of data. As a result, standard theoretical models and off-the-shelf algorithms become either impractical or intractable (and in some cases both).

While many problems are hard in the worst case, the problems of practical interest are often much more well-behaved, and have the potential to be modeled in ways that make them tractable. Indeed, many discrete problems in machine learning can possess beneficial structure; such structure has been an important ingredient in many successful (approximate) solution strategies. Examples include submodularity, marginal polytopes, symmetries and exchangeability.

Machine learning, algorithms, discrete mathematics and combinatorics as well as applications in computer vision, speech, NLP, biology and network analysis are all active areas of research, each with an increasingly large body of foundational knowledge. The workshop aims to ask questions that enable communication across these fields. In particular, this year we aim to address the investigation of combinatorial structures allows to capture complex, high-order dependencies in discrete learning problems prevalent in deep learning, social networks, etc. An emphasis will be given on uncertainty and structure that results from problem instances being estimated from data.

In the last year, generative machine learning and machine creativity have gotten a lot of attention in the non-research world. At the same time there have been significant advances in generative models for media creation and for design. This one-day workshop explores several issues in the domain of generative models for creativity and design. First, we will look at algorithms for generation and creation of new media and new designs, engaging researchers building the next generation of generative models (GANs, RL, etc) and also from a more information-theoretic view of creativity (compression, entropy, etc). Second, we will investigate the social and cultural impact of these new models, engaging researchers from HCI/UX communities. Finally, we’ll hear from some of the artists and musicians who are adopting machine learning approaches like deep learning and reinforcement learning as part of their artistic process. We’ll leave ample time for discussing both the important technical challenges of generative models for creativity and design, as well as the philosophical and cultural issues that surround this area of research.

Background
In 2016, DeepMind’s AlphaGo made two moves against Lee Sedol that were described by the Go community as “brilliant,” “surprising,” “beautiful,” and so forth. Moreover, there was little discussion surrounding the fact that these very creative moves were actually made by a machine (Wired); it was enough that they were great examples of go playing. At the same time, the general public showed more concern for other applications of generative models. Algorithms that allow for convincing voice style transfer (Lyrebird) or puppet-like video face control (Face2Face) have raised concerns that generative ML will be used to make convincing forms of fake news (FastCompany).

Balancing this, the arts and music worlds have positively embraced generative models. Starting with DeepDream and expanding with image and video generation advances (e.g. GANs) we’ve seen lots of new and interesting art and music [citations] technologies provided by the machine learning community. We’ve seen research projects like Google Brain’s Magenta, Sony CSL’s FlowMachines and IBM’s Watson undertake collaborations and attempt to build tools and ML models for use by these communities.

Research
Recent advances in generative models enable new possibilities in art and music production. Language models can be used to write science fiction film scripts (Sunspring) and even replicate the style of individual authors (Deep Tingle). Generative models for image and video allow us to create visions of people, places and things that resemble the distribution of actual images (GANs etc). Sequence modelling techniques have opened up the possibility of generating realistic musical scores (MIDI generation etc) and even raw audio that resembles human speech and physical instruments (DeepMind’s WaveNet, MILA’s Char2Wav and Google’s NSynth). In addition, sequence modelling allows us to model vector images to construct stroke-based drawings of common objects according to human doodles (sketch-rnn).

In addition to field-specific research, a number of papers have come out that are directly applicable to the challenges of generation and evaluation such as learning from human preferences (Christiano et al., 2017) and CycleGAN. The application of Novelty Search (Stanley), evolutionary complexification (Stanley - CPPN, NEAT, Nguyen et al - Plug&Play GANs, Innovation Engine) and intrinsic motivation (Oudeyer et al 2007, Schmidhuber on Fun and Creativity) techniques, where objective functions are constantly evolving, is still not common practice in art and music generation using machine learning.

Another focus of the workshop is how to better enable human influence over generative models. This could include learning from human preferences, exposing model parameters in ways that are understandable and relevant to users in a given application domain (e.g., similar to Morris et al. 2008), enabling users to manipulate models through changes to training data (Fiebrink et al. 2011), allowing users to dynamically mix between multiple generative models (Akten & Grierson 2016), or other techniques. Although questions of how to make learning algorithms controllable and understandable to users are relatively nacesent in the modern context of deep learning and reinforcement learning, such questions have been a growing focus of work within the human-computer interaction community (e.g., examined in a CHI 2016 workshop on Human-Centred Machine Learning), and the AI Safety community (e.g. Christiano et al. 2017, using human preferences to train deep reinforcement learning systems). Such considerations also underpin the new Google “People + AI Research” (PAIR) initiative.

Artists and Musicians
All the above techniques improve our capabilities of producing text, sound and images. Art and music that stands the test of time however requires more than that. Recent research includes a focus on novelty in creative adversarial networks (Elgammal et al., 2017) and considers how generative algorithms can integrate into human creative processes, supporting exploration of new ideas as well as human influence over generated content (Atken & Grierson 2016a, 2016b). Artists including Mario Klingemann, Gene Kogan, Mike Tyka, and Memo Akten have further contributed to this space of work by creating artwork that compellingly demonstrates capabilities of generative algorithms, and by publicly reflecting on the artistic affordances of these new tools.

The goal of this workshop is to bring together researchers interested in advancing art and music generation to present new work, foster collaborations and build networks.

In this workshop, we are particularly interested in how the following can be used in art and music generation: reinforcement learning, generative adversarial networks, novelty search and evaluation as well as learning from user preferences. We welcome submissions of short papers, demos and extended abstracts related to the above.

There will also be an open call for a display of artworks incorporating machine learning techniques.

While traditional computer security relies on well-defined attack models and proofs of security, a science of security for machine learning systems has proven more elusive. This is due to a number of obstacles, including (1) the highly varied angles of attack against ML systems, (2) the lack of a clearly defined attack surface (because the source of the data analyzed by ML systems is not easily traced), and (3) the lack of clear formal definitions of security that are appropriate for ML systems. At the same time, security of ML systems is of great import due the recent trend of using ML systems as a line of defense against malicious behavior (e.g., network intrusion, malware, and ransomware), as well as the prevalence of ML systems as parts of sensitive and valuable software systems (e.g., sentiment analyzers for predicting stock prices). This workshop will bring together experts from the computer security and machine learning communities in an attempt to highlight recent work in this area, as well as to clarify the foundations of secure ML and chart out important directions for future work and cross-community collaborations.

Whether it is biological networks of proteins and genes or technological ones like sensor networks and the Internet, we are surrounded today by complex systems composed of entities interacting with and affecting each other. An urgent need has therefore emerged for developing novel techniques for modeling, learning, and conducting inference in such networked systems. Consequently, we have seen progress from a variety of disciplines in both fundamental methodology and in applications of such methods to practical problems. However, much work remains to be done, and a unifying and principled framework for dealing with these problems remains elusive. This workshop aims to bring together theoreticians and practitioners in order to both chart out recent advances and to discuss new directions in understanding interactions in large and complex systems. NIPS, with its attendance by a broad and cross-disciplinary set of researchers offers the ideal venue for this exchange of ideas.

The workshop will feature a mix of contributed talks, contributed posters, and invited talks by leading researchers from diverse backgrounds working in these areas. We will also have a specific segment of the schedule reserved for the presentation of open problems, and will have plenty of time for discussions where we will explicitly look to spark off collaborations amongst the attendees.

We encourage submissions in a variety of topics including, but not limited to:
* Computationally and statistically efficient techniques for learning graphical models from data including convex, greedy, and active approaches.
* New probabilistic models of interacting systems including nonparametric and exponential family graphical models.
* Community detection algorithms including semi-supervised and adaptive approaches.
* Techniques for modeling and learning causal relationships from data.
* Bayesian techniques for modeling complex data and causal relationships.
* Kernel methods for directed and undirected graphical models.
* Applications of these methods in various areas like sensor networks, computer networks, social networks, and biological networks like phylogenetic trees and graphs.

Successful submissions will emphasize the role of statistical and computational learning to the problem at hand. The author(s) of these submissions will be invited to present their work as either a poster or as a contributed talk. Alongside these, we also solicit submissions of open problems that go with the theme of the workshop. The author(s) of the selected open problems will be able to present the problem to the attendees and solicit feedback/collaborations.

A new area is emerging at the intersection of artificial intelligence, machine learning, and systems design. This birth is driven by the explosive growth of diverse applications of ML in production, the continued growth in data volume, and the complexity of large-scale learning systems. The goal of this workshop is to bring together experts working at the crossroads of machine learning, system design and software engineering to explore the challenges faced when building practical large-scale ML systems. In particular, we aim to elicit new connections among these diverse fields, and identify tools, best practices and design principles. We also want to think about how to do research in this area and properly evaluate it. The workshop will cover ML and AI platforms and algorithm toolkits, as well as dive into machine learning-focused developments in distributed learning platforms, programming languages, data structures, GPU processing, and other topics.

This workshop will follow the successful model we have previously run at ICML, NIPS and SOSP 2017.

Our plan is to run this workshop annually at one ML venue and one Systems venue, and eventually merge these communities into a full conference venue. We believe this dual approach will help to create a low barrier to participation for both communities.

Extracting knowledge from text, images, audio, and video and translating these extractions into a coherent, structured knowledge base (KB) is a task that spans the areas of machine learning, natural language processing, computer vision, databases, search, data mining and artificial intelligence. Over the past two decades, machine learning techniques used for information extraction, graph construction, and automated knowledge base construction have evolved from simple rule learning to end-to-end neural architectures with papers on the topic consistently appearing at NIPS. Hence, we believe this workshop will appeal to NIPS attendees and be a valuable contribution.

Furthermore, there has been significant interest and investment in knowledge base construction in both academia and industry in recent years. Most major internet companies and many startups have developed knowledge bases that power digital assistants (e.g. Siri, Alexa, Google Now) or provide the foundations for search and discovery applications. A similarly abundant set of knowledge systems have been developed at top universities such as Stanford (DeepDive), Carnegie Mellon (NELL), the University of Washington (OpenIE), the University of Mannheim (DBpedia), and the Max Planck Institut Informatik (YAGO, WebChild), among others. Our workshop serves as a forum for researchers working on knowledge base construction in both academia and industry.

With this year’s workshop we would like to continue the successful tradition of the previous five AKBC workshops. AKBC fills a unique need in the field, bringing together industry leaders and academic researchers. Our workshop is focused on stellar invited talks from high-profile speakers who identify the pressing research areas where current methods fall short and propose visionary approaches that will lead to the next generation of knowledge bases. Our workshop prioritizes a participatory environment where attendees help identify the most promising research, contribute to surveys on controversial questions, and suggest debate topics for speaker panels. In addition, for the first time, AKBC will address a longstanding issue in the AKBC, that of equitable comparison and evaluation across methods, by including a shared evaluation platform, Stanford’s KBP Online (https://kbpo.stanford.edu/), which will allow crowdsourced labels for KBs without strong assumptions about the data or methods used. Together, this slate of high-profile research talks, outstanding contributed papers, an interactive research environment, and a novel evaluation service will ensure AKBC is a popular addition to the NIPS program.

Many modern methods for prediction leverage nearest neighbor (NN) search to find past training examples most similar to a test example, an idea that dates back in text to at least the 11th century in the “Book of Optics” by Alhazen. Today, NN methods remain popular, often as a cog in a bigger prediction machine, used for instance in recommendation systems, forecasting baseball player performance and election outcomes, survival analysis in healthcare, image in-painting, crowdsourcing, graphon estimation, and more. The popularity of NN methods is due in no small part to the proliferation of high-quality fast approximate NN search methods that scale to high-dimensional massive datasets typical of contemporary applications. Moreover, NN prediction readily pairs with methods that learn similarities, such as metric learning methods or Siamese networks. In fact, some well-known pairings that result in nearest neighbor predictors that learn similarities include random forests and many boosting methods.

Despite the popularity, success, and age of nearest neighbor methods, our theoretical understanding of them is still surprisingly incomplete (perhaps much to the chagrin of the initial efforts of analysis by Fix, Hodges, Cover, and Hart) and can also be disconnected from what practitioners actually want or care about. Many successful approximate nearest neighbor methods in practice do not have known theoretical guarantees, and many of the guarantees for exact nearest neighbor methods do not readily handle approximation. Meanwhile, many applications use variations on NN methods, for which existing theory may not extend to, or for which existing theory is not easily usable by a practitioner. Suffice it to say, a lot is lost in translation between different communities working with NN methods.

In short, NN methods is an exciting field at the intersection of classical statistics, machine learning, data structures and domain specific expertise. The aim of this work is to bring together theoreticians and practitioners alike from these various different backgrounds with a diverse range of perspectives to bring everyone up to speed on:
- Best known statistical/computational guarantees (especially recent non-asymptotic results)
- Latest methods/systems that have been developed especially for fast approximate NN search that scale to massive datasets
- Various applications in which NN methods are heavily used as a critical component in prediction or inference

By gathering a diverse crowd, we hope attendees share their perspectives, identify ways to bridge theory and practice, and discuss avenues of future research.

In recent years robotics has made significant strides towards applications of real value to the public domain. Robots are now increasingly expected to work for and alongside us in complex, dynamic environments. Machine learning has been a key enabler of this success, particularly in the realm of robot perception where, due to substantial overlap with the machine vision community, methods and training data can be readily leveraged.

Recent advances in reinforcement learning and learning from demonstration — geared towards teaching agents how to act — provide a tantalising glimpse at a promising future trajectory for robot learning. Mastery of challenges such as the Atari suite and AlphaGo build significant excitement as to what our robots may be able to do for us in the future. However, this success relies on the ability of learning cheaply, often within the confines of a virtual environment, by trial and error over as many episodes as required. This presents a significant challenge for embodied systems acting and interacting in the real world. Not only is there a cost (either monetary or in terms of execution time) associated with a particular trial, thus limiting the amount of training data obtainable, but there also exist safety constraints which make an exploration of the state space simply unrealistic: teaching a real robot to cross a real road via reinforcement learning for now seems a noble yet somewhat far fetched goal. A significant gulf therefore exists between prior art on teaching agents to act and effective approaches to real-world robot learning. This, we posit, is one of the principal impediments at the moment in advancing real-world robotics science.

In order to bridge this gap researchers and practitioners in robot learning have to address a number of key challenges to allow real-world systems to be trained in a safe and data-efficient manner. This workshop aims to bring together experts in reinforcement learning, learning from demonstration, deep learning, field robotics and beyond to discuss what the principal challenges are and how they might be addressed. With a particular emphasis on data efficient learning, of particular interest will be contributions in representation learning, curriculum learning, task transfer, one-shot learning, domain transfer (in particular from simulation to real-world tasks), reinforcement learning for real world applications, learning from demonstration for real world applications, knowledge learning from observation and interaction, active concept acquisition and learning causal models.

The increased variability of acquired data has recently pushed the field of machine learning to extend its scope to non-standard data including for example functional (Ferraty & Vieu, 2006; Wang et al., 2015), distributional (Póczos et al., 2013), graph, or topological data (Carlsson, 2009; Vitaliy). Successful applications span across a wide range of disciplines such as healthcare (Zhou et al., 2013), action recognition from iPod/iPhone accelerometer data (Sun et al., 2013), causal inference (Lopez-Paz et al., 2015), bioinformatics (Kondor & Pan, 2016; Kusano et al., 2016), cosmology (Ravanbakhsh et al., 2016; Law et al., 2017), acoustic-to-articulatory speech inversion (Kadri et al., 2016), network inference (Brouard et al., 2016), climate research (Szabó et al., 2016), and ecological inference (Flaxman et al., 2015).

Leveraging the underlying structure of these non-standard data types often leads to significant boost in prediction accuracy and inference performance. In order to achieve these compelling improvements, however, numerous challenges and questions have to be addressed: (i) choosing an adequate representation of the data, (ii) constructing appropriate similarity measures (inner product, norm or metric) on these representations, (iii) efficiently exploiting their intrinsic structure such as multi-scale nature or invariances, (iv) designing affordable computational schemes (relying e.g., on surrogate losses), (v) understanding the computational-statistical tradeoffs of the resulting algorithms, and (vi) exploring novel application domains.

The goal of this workshop is
(i) to discuss new theoretical considerations and applications related to learning with non-standard data,
(ii) to explore future research directions by bringing together practitioners with various domain expertise and algorithmic tools, and theoreticians interested in providing sound methodology,
(iii) to accelerate the advances of this recent area and application arsenal.

We encourage submissions on a variety of topics, including but not limited to:
- Novel applications for learning on non-standard objects
- Learning theory/algorithms on distributions
- Topological and geometric data analysis
- Functional data analysis
- Multi-task learning, structured output prediction, and surrogate losses
- Vector-valued learning (e.g., operator-valued kernel)
- Gaussian processes
- Learning on graphs and networks
- Group theoretic methods and invariances in learning
- Learning with non-standard input/output data
- Large-scale approximations (e.g. sketching, random Fourier features, hashing, Nyström method, inducing point methods), and statistical-computational efficiency tradeoffs


References:

Frédéric Ferraty and Philippe Vieu. Nonparametric Functional Data Analysis: Theory and Practice. Springer Series in Statistics, Springer-Verlag, 2006.

Jane-Ling Wang, Jeng-Min Chiou, and Hans-Georg Müller. Review of Functional Data Analysis. Annual Review of Statistics, 3:1-41, 2015.

Barnabás Póczos, Aarti Singh, Alessandro Rinaldo, Larry Wasserman. Distribution-free Distribution Regression. International Conference on AI and Statistics (AISTATS), PMLR 31:507-515, 2013.

Gunnar Carlsson. Topology and data. Bulletin of the American Mathematical Society, 46 (2): 255-308, 2009.

Vitaliy Kurlin. Research blog: http://kurlin.org/blog/.

Jiayu Zhou, Jun Liu, Vaibhav A. Narayan, and Jieping Ye. Modeling disease progression via multi-task learning. NeuroImage, 78:233-248, 2013.

Xu Sun, Hisashi Kashima, and Naonori Ueda. Large-scale personalized human activity recognition using online multitask learning. IEEE Transactions on Knowledge and Data Engine, 25:2551-2563, 2013.

David Lopez-Paz, Krikamol Muandet, Bernhard Schölkopf, and Ilya Tolstikhin. Towards a Learning Theory of Cause-Effect Inference. International Conference on Machine Learning (ICML), PMLR 37:1452-1461, 2015.

Risi Kondor, Horace Pan. The Multiscale Laplacian Graph Kernel. Advances in Neural Information Processing Systems (NIPS), 2982-2990, 2016.

Genki Kusano, Yasuaki Hiraoka, Kenji Fukumizu. Persistence weighted Gaussian kernel for topological data analysis. International Conference on Machine Learning (ICML), PMLR 48:2004-2013, 2016.

Siamak Ravanbakhsh, Junier Oliva, Sebastian Fromenteau, Layne Price, Shirley Ho, Jeff Schneider, Barnabás Póczos. Estimating Cosmological Parameters from the Dark Matter Distribution. International Conference on Machine Learning (ICML), PMLR 48:2407-2416, 2016.

Ho Chung Leon Law, Dougal J. Sutherland, Dino Sejdinovic, Seth Flaxman. Bayesian Distribution Regression. Technical Report, 2017 (https://arxiv.org/abs/1705.04293).

Hachem Kadri, Emmanuel Duflos, Philippe Preux, Stéphane Canu, Alain Rakotomamonjy, and Julien Audiffren. Operator-valued kernels for learning from functional response data. Journal of Machine Learning Research, 17:1-54, 2016.

Céline Brouard, Marie Szafranski, and Florence d’Alché-Buc. Input output kernel regression: Supervised and semi-supervised structured output prediction with operator-valued kernels. Journal of Machine Learning Research, 17:1-48, 2016.

Zoltán Szabó, Bharath K. Sriperumbudur, Barnabás Póczos, Arthur Gretton. Learning Theory for Distribution Regression. Journal of Machine Learning Research, 17(152):1-40, 2016.

Seth Flaxman, Yu-Xiang Wang, and Alex Smola. Who supported Obama in 2012? Ecological inference through distribution regression. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 289-298, 2015.

Data, in the form of time-dependent sequential observations emerge in many key real-world problems, ranging from biological data, financial markets, weather forecasting to audio/video processing. However, despite the ubiquity of such data, most mainstream machine learning algorithms have been primarily developed for settings in which sample points are drawn i.i.d. from some (usually unknown) fixed distribution. While there exist algorithms designed to handle non-i.i.d. data, these typically assume specific parametric form for the data-generating distribution. Such assumptions may undermine the complex nature of modern data which can possess long-range dependency patterns, and for which we now have the computing power to discern. On the other extreme lie on-line learning algorithms that consider a more general framework without any distributional assumptions. However, by being purely-agnostic, common on-line algorithms may not fully exploit the stochastic aspect of time-series data.

This is the third instalment of time series workshop at NIPS and will build on the success of the previous events: NIPS 2015 Time Series Workshop and NIPS 2016 Time Series Workshop.

The goal of this workshop is to bring together theoretical and applied researchers interested in the analysis of time series and development of new algorithms to process sequential data. This includes algorithms for time series prediction, classification, clustering, anomaly and change point detection, correlation discovery, dimensionality reduction as well as a general theory for learning and comparing stochastic processes. We invite researchers from the related areas of batch and online learning, reinforcement learning, data analysis and statistics, econometrics, and many others to contribute to this workshop.

This is the first NIPS edition on "NIPS Competitions". We received 23 competition proposals related to data-driven and live competitions on different aspects of NIPS. Proposals were reviewed by several qualified researchers and experts in challenge organization. Five top-scored competitions were accepted to be run and present their results during the NIPS 2017 Competition track day. Evaluation was based on the quality of data, problem interest and impact, promoting the design of new models, and a proper schedule and managing procedure. Below, you can find the five accepted competitions. Organizers and participants in these competitions will be invited to present their work to this workshop, to be held on December 8th.

Accepted competitions:
The Conversational Intelligence Challenge Webpage: http://convai.io
Classifying Clinically Actionable Genetic Mutations Webpage: https://www.kaggle.com/c/msk-redefining-cancer-treatment
Learning to Run Webpage: https://www.crowdai.org/challenges/nips-2017-learning-to-run
Human-Computer Question Answering Competition Webpage: http://sites.google.com/view/hcqa/
Adversarial Attacks and Defences Webpage: https://www.kaggle.com/nips-2017-adversarial-learning-competition

Abstracts and full papers: http://media.aau.dk/smc/ml4audio/

Audio signal processing is currently undergoing a paradigm change, where data-driven machine learning is replacing hand-crafted feature design. This has led some to ask whether audio signal processing is still useful in the "era of machine learning." There are many challenges, new and old, including the interpretation of learned models in high dimensional spaces, problems associated with data-poor domains, adversarial examples, high computational requirements, and research driven by companies using large in-house datasets that is ultimately not reproducible.

ML4Audio aims to promote progress, systematization, understanding, and convergence of applying machine learning in the area of audio signal processing. Specifically, we are interested in work that demonstrates novel applications of machine learning techniques to audio data, as well as methodological considerations of merging machine learning with audio signal processing. We seek contributions in, but not limited to, the following topics:
- audio information retrieval using machine learning;
- audio synthesis with given contextual or musical constraints using machine learning;
- audio source separation using machine learning;
- audio transformations (e.g., sound morphing, style transfer) using machine learning;
- unsupervised learning, online learning, one-shot learning, reinforcement learning, and incremental learning for audio;
- applications/optimization of generative adversarial networks for audio;
- cognitively inspired machine learning models of sound cognition;
- mathematical foundations of machine learning for audio signal processing.

This workshop especially targets researchers, developers and musicians in academia and industry in the area of MIR, audio processing, hearing instruments, speech processing, musical HCI, musicology, music technology, music entertainment, and composition.

ML4Audio Organisation Committee:
Hendrik Purwins, Aalborg University Copenhagen, Denmark (hpu@create.aau.dk)
Bob L. Sturm, Queen Mary University of London, UK (b.sturm@qmul.ac.uk)
Mark Plumbley, University of Surrey, UK (m.plumbley@surrey.ac.uk)

Program Committee:
Abeer Alwan (University of California, Los Angeles)
Jon Barker (University of Sheffield)
Sebastian Böck (Johannes Kepler University Linz)
Mads Græsbøll Christensen (Aalborg University)
Maximo Cobos (Universitat de Valencia)
Sander Dieleman (Google DeepMind)
Monika Dörfler (University of Vienna)
Shlomo Dubnov (UC San Diego)
Philippe Esling (IRCAM)
Cédric Févotte (IRIT)
Emilia Gómez (Universitat Pompeu Fabra)
Emanuël Habets (International Audio Labs Erlangen)
Jan Larsen (Danish Technical University)
Marco Marchini (Spotify)
Rafael Ramirez (Universitat Pompeu Fabra)
Gaël Richard (TELECOM ParisTech)
Fatemeh Saki (UT Dallas)
Sanjeev Satheesh (Baidu SVAIL)
Jan Schlüter (Austrian Research Institute for Artificial Intelligence)
Joan Serrà (Telefonica)
Malcolm Slaney (Google)
Emmanuel Vincent (INRIA Nancy)
Gerhard Widmer (Austrian Research Institute for Artificial Intelligence)
Tao Zhang (Starkey Hearing Technologies)

Six billion people live in developing world countries. The unique development challenges faced by these regions have long been studied by researchers ranging from sociology to statistics and ecology to economics. With the emergence of mature machine learning methods in the past decades, researchers from many fields - including core machine learning - are increasingly turning to machine learning to study and address challenges in the developing world. This workshop is about delving into the intersection of machine learning and development research.

Machine learning present tremendous potential to development research and practice. Supervised methods can provide expert telemedicine decision support in regions with few resources; deep learning techniques can analyze satellite imagery to create novel economic indicators; NLP algorithms can preserve and translate obscure languages, some of which are only spoken. Yet, there are notable challenges with machine learning in the developing world. Data cleanliness, computational capacity, power availability, and internet accessibility are more limited than in developed countries. Additionally, the specific applications differ from what many machine learning researchers normally encounter. The confluence of machine learning's immense potential with the practical challenges posed by developing world settings has inspired a growing body of research at the intersection of machine learning and the developing world.

This one-day workshop is focused on machine learning for the developing world, with an emphasis on developing novel methods and technical applications that address core concerns of developing regions. We will consider a wide range of development areas including health, education, institutional integrity, violence mitigation, economics, societal analysis, and environment. From the machine learning perspective we are open to all methodologies with an emphasis on novel techniques inspired by particular use cases in the developing world.

Invited speakers will address particular areas of interest, while poster sessions and a guided panel discussion will encourage interaction between attendees. We wish to review the current approaches to machine learning in the developing world, and inspire new approaches and paradigms that can lay the groundwork for substantial innovation.

Extreme classification is a rapidly growing research area focussing on multi-class and multi-label problems involving an extremely large number of labels. Many applications have been found in diverse areas ranging from language modelling to document tagging in NLP, face recognition to learning universal feature representations in computer vision, gene function prediction in bioinformatics, etc. Extreme classification has also opened up a new paradigm for ranking and recommendation by reformulating them as multi- label learning tasks where each item to be ranked or recommended is treated as a separate label. Such reformulations have led to significant gains over traditional collaborative filtering and content based recommendation techniques. Consequently, extreme classifiers have been deployed in many real-world applications in industry.

Extreme classification raises a number of interesting research questions including those related to:

* Large scale learning and distributed and parallel training
* Log-time and log-space prediction and prediction on a test-time budget
* Label embedding and tree based approaches
* Crowd sourcing, preference elicitation and other data gathering techniques
* Bandits, semi-supervised learning and other approaches for dealing with training set biases and label noise
* Bandits with an extremely large number of arms
* Fine-grained classification
* Zero shot learning and extensible output spaces
* Tackling label polysemy, synonymy and correlations
* Structured output prediction and multi-task learning
* Learning from highly imbalanced data
* Dealing with tail labels and learning from very few data points per label
* PU learning and learning from missing and incorrect labels
* Feature extraction, feature sharing, lazy feature evaluation, etc.
* Performance evaluation
* Statistical analysis and generalization bounds
* Applications to new domains

The workshop aims to bring together researchers interested in these areas to encourage discussion and improve upon the state-of-the-art in extreme classification. In particular, we aim to bring together researchers from the natural language processing, computer vision and core machine learning communities to foster interaction and collaboration. Several leading researchers will present invited talks detailing the latest advances in the area. We also seek extended abstracts presenting work in progress which will be reviewed for acceptance as a spotlight + poster or a talk. The workshop should be of interest to researchers in core supervised learning as well as application domains such as recommender systems, computer vision, computational advertising, information retrieval and natural language processing. We expect a healthy participation from both industry and academia.

Machine deception refers to the capacity for machine learning systems to manipulate human and machine agents into believing, acting upon or otherwise accepting false information. The development of machine deception has had a long, foundational and under-appreciated impact on shaping research in the field of artificial intelligence. Thought experiments such as Alan Turing’s eponymous “Turing test” - where an automated system attempts to deceive a human judge into believing it is a human interlocutor, or Searle’s “Chinese room” - in which a human operator attempts to imbue the false impression of consciousness in a machine, are simultaneously exemplars of machine deception and some of the most famous and influential concepts in the field of AI.

As the field of machine learning advances, so too does machine deception seem poised to give rise to a host of practical opportunities and concerns. Machine deception can have many benign and beneficial applications. Chatbots designed to mimic human agents offer technical support and even provide therapy at a cost and scale that may not be otherwise achievable. On the other hand, the rise of techniques that leverage bots and other autonomous agents to manipulate and shape political speech online, has put machine deception in the political spotlight and raised fundamental questions regarding the ability to preserve truth in the digital domain. These concerns are amplified by recent demonstrations of machine learning techniques that synthesize hyper-realistic manipulations of audio and video.

The proposed workshop will bring together research at the forefront of machine deception, including:

Machine-machine deception: Where a machine agent deceives another machine agent, e.g. the use of “bot farms” that automate posting on social media platforms to manipulate content ranking algorithms or evolutionary networks to generate images that “fool” deep neural networks.
Human-machine deception: Where a human agent deceives a machine agent, e.g. the use of human “troll farms” to manipulate content ranking algorithms or use of adversarial examples to exploit fragility of autonomous systems (e.g. stop sign sticker for self driving cars or printed eye-glasses for facial recognition).
Machine-human deception: Where a machine agent is leveraged to deceives a human agent, e.g. the use of GANs to produce realistic manipulations of audio and video content.

Although the workshop will primarily focus on the technical aspects of machine deception, submissions from the fields of law, policy, social sciences and psychology will also be encouraged. It is envisaged that this interdisciplinary forum will both shine a light on what is possible given state of the art tools today, and provide instructive guidance for both technologists and policy-makers going forward.

Everyday interactions require a common understanding of language, i.e. for people to communicate effectively, words (for example ‘cat’) should invoke similar beliefs over physical concepts (what cats look like, the sounds they make, how they behave, what their skin feels like etc.). However, how this ‘common understanding’ emerges is still unclear.

One appealing hypothesis is that language is tied to how we interact with the environment. As a result, meaning emerges by ‘grounding’ language in modalities in our environment (images, sounds, actions, etc.).

Recent concurrent works in machine learning have focused on bridging visual and natural language understanding through visually-grounded language learning tasks, e.g. through natural images (Visual Question Answering, Visual Dialog), or through interactions with virtual physical environments. In cognitive science, progress in fMRI enables creating a semantic atlas of the cerebral cortex, or to decode semantic information from visual input. And in psychology, recent studies show that a baby’s most likely first words are based on their visual experience, laying the foundation for a new theory of infant language acquisition and learning.

As the grounding problem requires an interdisciplinary attitude, this workshop aims to gather researchers with broad expertise in various fields — machine learning, computer vision, natural language, neuroscience, and psychology — to discuss their cutting edge work as well as perspectives on future directions in this exciting space of grounding and interactions.

We will accept papers related to:
— language acquisition or learning through interactions
— visual captioning, dialog, and question-answering
— reasoning in language and vision
— visual synthesis from language
— transfer learning in language and vision tasks
— navigation in virtual worlds with natural-language instructions
— machine translation with visual cues
— novel tasks that combine language, vision and actions
— understanding and modeling the relationship between language and vision in humans
— semantic systems and modeling of natural language and visual stimuli representations in the human brain

Important dates
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Submission deadline: 3rd November 2017
Extended Submission deadline: 17th November 2017

Acceptance notification (First deadline): 10th November 2017
Acceptance notification (Second deadline): 24th November 2017

Workshop: 8th December 2017

Paper details
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— Contributed papers may include novel research, preliminary results, extended abstract, positional papers or surveys
— Papers are limited to 4 pages, excluding references, in the latest camera-ready NIPS format: https://nips.cc/Conferences/2017/PaperInformation/StyleFiles
— Papers published at the main conference can be submitted without reformatting
— Please submit via email: nips2017vigil@gmail.com


Accepted papers
-----------------------
— All accepted papers will be presented during 2 poster sessions
— Up to 5 accepted papers will be invited to deliver short talks
— Accepted papers will be made publicly available as non-archival reports, allowing future submissions to archival conferences and journals


Invited Speakers
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Raymond J. Mooney - University of Texas
Sanja Fidler - University of Toronto
Olivier Pietquin - DeepMind
Jack Gallant - University of Berkeley
Devi Parikh - Georgia Tech / FAIR
Felix Hill - DeepMind
Jack Gallant - Univeristy of Berkeley
Chen Yu - University of Indiana

In the span of only a few years, conversational systems have become commonplace. Every day, millions of people use natural-language interfaces such as Siri, Google Now, Cortana, Alexa, Facebook M and others via in-home devices, phones, or messaging channels such as Messenger, Slack, Skype, among others.  At the same time, interest among the research community in conversational systems has blossomed: for supervised and reinforcement learning, conversational systems often serve as both a benchmark task and an inspiration for new ML methods at conferences which don't focus on speech and language per se, such as NIPS, ICML, IJCAI, and others.  Research community challenge tasks are proliferating, including the sixth Dialog Systems Technology Challenge (DSTC6), the Amazon Alexa prize, and the Conversational Intelligence Challenge live competition at NIPS 2017. 

Now more than ever, it is crucial to promote cross-pollination of ideas between academic research centers and industry. The goal of this workshop is to bring together researchers and practitioners in this area, to clarify impactful research problems, share findings from large-scale real-world deployments, and generate new ideas for future lines of research.   

This workshop will include invited talks from academia and industry, contributed work, and open discussion.  In these talks, senior technical leaders from many of the most popular conversational services will give insights into real usage and challenges at scale.  An open call for papers will be issued, and we will prioritize forward-looking papers that propose interesting and impactful contributions.  We will end the day with an open discussion, including a panel consisting of academic and industrial researchers.

Machine learning is primarily concerned with the design and analysis of algorithms that learn about an entity. Increasingly more, machine learning is being used to design policies that affect the entity it once learned about. This can cause the entity to react and present a different behavior. Ignoring such interactions could lead to solutions that are ultimately ineffective in practice. For example, to design an effective ad display one has to take into account how a viewer would react to the displayed advertisements, for example by choosing to scroll through or click on them. Additionally, in many environments, multiple learners learn concurrently about one or more related entities. This can bring about a range of interactions between individual learners. For example, multiple firms may compete or collaborate on performing market research. How do the learners and entities interact? How do these interactions change the task at hand? What are some desirable interactions in a learning environment? And what are the mechanisms for bringing about such desirable interactions? These are some of the questions we would like to explore more in this workshop.

Traditionally, learning theory has adopted two extreme views in this respect: First, when learning occurs in isolation from strategic behavior, such as in the classical PAC setting where the data is drawn from a fixed distribution; second, when the learner faces an adversary whose goal is to inhibit the learning process, such as the minimax setting where the data is generated by an adaptive worst-case adversary. While these extreme perspectives have lead to elegant results and concepts, such as VC dimension, Littlestone dimension, regret bounds, and more, many types of problems that we would like to solve involve strategic behaviors that do not fall into these two extremes. Examples of these problems include but are not limited to

1. Learning from data that is produced by agents who have vested interest in the outcome or the learning process. For example, learning a measure of quality of universities by surveying members of the academia who stand to gain or lose from the outcome, or when a GPS routing app has to learn patterns of traffic delay by routing individuals who have no interest in taking slower routes.

2. Learning a model for the strategic behavior of one or more agents by observing their interactions, for example, learning economical demands of buyers by observing their bidding patterns when competing with other buyers.

3. Learning as a model of interactions between agents. Examples of this include applications to swarm robotics, where individual agents have to learn to interact in a multi-agent setting in order to achieve individual or collective goals.

4. Interactions between multiple learners. In many settings, two or more learners learn about the same or multiple related concepts. How do these learners interact? What are the scenarios under which they would share knowledge, information, or data. What are the desirable interactions between learners? As an example, consider multiple competing pharmaceutical firms that are learning about the effectiveness of a certain treatment. In this case, while competing firms would prefer not to share their findings, it is beneficial to the society when such findings are shared. How can we incentivize these learners to perform such desirable interactions?

The main goal of this workshop is to address current challenges and opportunities that arise from the presence of strategic behavior in learning theory. This workshop aims at bringing together members of different communities, including machine learning, economics, theoretical computer science, and social computing, to share recent results, discuss important directions for future research, and foster collaborations.

Physical sciences span problems and challenges at all scales in the universe: from finding exoplanets and asteroids in trillions of sky-survey pixels, to automatic tracking of extreme weather phenomena in climate datasets, to detecting anomalies in event streams from the Large Hadron Collider at CERN. Tackling a number of associated data-intensive tasks, including, but not limited to, regression, classification, clustering, dimensionality reduction, likelihood-free inference, generative models, and experimental design are critical for furthering scientific discovery. The Deep Learning for Physical Sciences (DLPS) workshop invites researchers to contribute papers that demonstrate progress in the application of machine and deep learning techniques to real-world problems in physical sciences (including the fields and subfields of astronomy, chemistry, Earth science, and physics).

We will discuss research questions, practical implementation challenges, performance / scaling, and unique aspects of processing and analyzing scientific datasets. The target audience comprises members of the machine learning community who are interested in scientific applications and researchers in the physical sciences. By bringing together these two communities, we expect to strengthen dialogue, introduce exciting new open problems to the wider NIPS community, and stimulate production of new approaches to solving science problems. Invited talks from leading individuals from both communities will cover the state-of-the-art techniques and set the stage for this workshop.

The use of machine learning has become pervasive in our society, from specialized scientific data analysis to industry intelligence and practical applications with a direct impact in the public domain. This impact involves different social issues including privacy, ethics, liability and accountability. This workshop aims to discuss the use of machine learning in safety critical environments, with special emphasis on three main application domains:
- Healthcare
- Autonomous systems
- Complainants and liability in data driven industries
We aim to answer some of these questions: How do we make our models more comprehensible and transparent? Shall we always trust our decision making process? How do we involve field experts in the process of making machine learning pipelines more practically interpretable from the viewpoint of the application domain?

The success of machine learning has been demonstrated time and time again in classification, generative modelling, and reinforcement learning. In particular, we have recently seen interesting developments where ML has been applied to the natural sciences (chemistry, physics, materials science, neuroscience and biology). Here, often the data is not abundant and very costly. This workshop will focus on the unique challenges of applying machine learning to molecules and materials.

Accurate prediction of chemical and physical properties is a crucial ingredient toward rational compound design in chemical and pharmaceutical industries. Many discoveries in chemistry can be guided by screening large databases of computational molecular structures and properties, but high level quantum-chemical calculations can take up to several days per molecule or material at the required accuracy, placing the ultimate achievement of in silico design out of reach for the foreseeable future. In large part the current state of the art for such problems is the expertise of individual researchers or at best highly-specific rule-based heuristic systems. Efficient methods in machine learning, applied to property and structure prediction, can therefore have pivotal impact in enabling chemical discovery and foster fundamental insights.

Because of this, in the past few years there has been a flurry of recent work towards designing machine learning techniques for molecule [1, 2, 4-11, 13-18, 20, 21, 23-32, 34-38] and material data [1-3, 5, 6, 12, 19, 24, 33]. These works have drawn inspiration from and made significant contributions to areas of machine learning as diverse as learning on graphs to models in natural language processing. Recent advances enabled the acceleration of molecular dynamics simulations, contributed to a better understanding of interactions within quantum many-body systems and increased the efficiency of density functional theory based quantum mechanical modeling methods. This young field offers unique opportunities for machine learning researchers and practitioners, as it presents a wide spectrum of challenges and open questions, including but not limited to representations of physical systems, physically constrained models, manifold learning, interpretability, model bias, and causality.

The goal of this workshop is to bring together researchers and industrial practitioners in the fields of computer science, chemistry, physics, materials science, and biology all working to innovate and apply machine learning to tackle the challenges involving molecules and materials. In a highly interactive format, we will outline the current frontiers and present emerging research directions. We aim to use this workshop as an opportunity to establish a common language between all communities, to actively discuss new research problems, and also to collect datasets by which novel machine learning models can be benchmarked. The program is a collection of invited talks, alongside contributed posters. A panel discussion will provide different perspectives and experiences of influential researchers from both fields and also engage open participant conversation. An expected outcome of this workshop is the interdisciplinary exchange of ideas and initiation of collaboration.


References
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[3] Kang, B., Ceder, G. (2009). Battery materials for ultrafast charging and discharging. Nature, 458(7235), 190.
[4] Bartók, A. P., Payne, M. C., Kondor, R., Csányi, G. (2010). Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons. Phys. Rev. Lett., 104(13), 136403.
[5] Behler, J. (2011). Atom-centered symmetry functions for constructing high-dimensional neural network potentials. J. Chem. Phys, 134(7), 074106.
[6] Behler, J. (2011). Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations. Phys. Chem. Chem. Phys., 13(40), 17930-17955.
[7] Rupp, M., Tkatchenko, A., Müller, K.-R., von Lilienfeld, O. A. (2012). Fast and accurate modeling of molecular atomization energies with machine learning. Phys. Rev. Lett., 108(5), 058301.
[8] Snyder, J. C., Rupp, M., Hansen, K., Müller, K.-R., Burke, K. (2012). Finding density functionals with machine learning. Phys. Rev. Lett., 108(25), 253002.
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[11] Bartók, A. P., Kondor, R., Csányi, G. (2013). On representing chemical environments. Phys. Rev. B, 87(18), 184115.
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[13] Ramsundar, B., Kearnes, S., Riley, P., Webster, D., Konerding, D., Pande, V. (2015). Massively multitask networks for drug discovery. arXiv preprint arXiv:1502.02072.
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[15] V. Botu, R. Ramprasad (2015). Learning scheme to predict atomic forces and accelerate materials simulations., Phys. Rev. B, 92(9), 094306.
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[18] Duvenaud, D. K., Maclaurin, D., Aguilera-Iparraguirre, J., Gomez-Bombarelli, R., Hirzel, T., Aspuru-Guzik, A., Adams, R. P. (2015). Convolutional networks on graphs for learning molecular fingerprints. NIPS, 2224-2232.
[19] Faber F. A., Lindmaa A., von Lilienfeld, O. A., Armiento, R. (2016). Machine learning energies of 2 million elpasolite (A B C 2 D 6) crystals. Phys. Rev. Lett., 117(13), 135502.
[20] Gomez-Bombarelli, R., Duvenaud, D., Hernandez-Lobato, J. M., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., Aspuru-Guzik, A. (2016). Automatic chemical design using a data-driven continuous representation of molecules. arXiv preprint arXiv:1610.02415.
[21] Wei, J. N., Duvenaud, D, Aspuru-Guzik, A. (2016). Neural networks for the prediction of organic chemistry reactions. ACS Cent. Sci., 2(10), 725-732.
[22] Sadowski, P., Fooshee, D., Subrahmanya, N., Baldi, P. (2016). Synergies between quantum mechanics and machine learning in reaction prediction. J. Chem. Inf. Model., 56(11), 2125-2128.
[23] Lee, A. A., Brenner, M. P., Colwell L. J. (2016). Predicting protein-ligand affinity with a random matrix framework. Proc. Natl. Acad. Sci., 113(48), 13564-13569.
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[26] Schütt, K. T., Arbabzadah, F., Chmiela, S., Müller, K.-R., Tkatchenko, A. (2017). Quantum-chemical insights from deep tensor neural networks. Nat. Commun., 8, 13890.
[27] Segler, M. H., Waller, M. P. (2017). Neural‐symbolic machine learning for retrosynthesis and reaction prediction. ‎Chem. Eur. J., 23(25), 5966-5971.
[28] Kusner, M. J., Paige, B., Hernández-Lobato, J. M. (2017). Grammar variational autoencoder. arXiv preprint arXiv:1703.01925.
[29] Coley, C. W., Barzilay, R., Jaakkola, T. S., Green, W. H., Jensen K. F. (2017). Prediction of organic reaction outcomes using machine learning. ACS Cent. Sci., 3(5), 434-443.
[30] Altae-Tran, H., Ramsundar, B., Pappu, A. S., Pande, V. (2017). Low data drug discovery with one-shot learning. ACS Cent. Sci., 3(4), 283-293.
[31] Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., Dahl, G. E. (2017). Neural message passing for quantum chemistry. arXiv preprint arXiv:1704.01212.
[32] Chmiela, S., Tkatchenko, A., Sauceda, H. E., Poltavsky, Igor, Schütt, K. T., Müller, K.-R. (2017). Machine learning of accurate energy-conserving molecular force fields. Sci. Adv., 3(5), e1603015.
[33] Ju, S., Shiga T., Feng L., Hou Z., Tsuda, K., Shiomi J. (2017). Designing nanostructures for phonon transport via bayesian optimization. Phys. Rev. X, 7(2), 021024.
[34] Ramakrishnan, R, von Lilienfeld, A. (2017). Machine learning, quantum chemistry, and chemical space. Reviews in Computational Chemistry, 225-256.
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[36] Smith, J., Isayev, O., Roitberg, A. E. (2017). ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost. Chem. Sci., 8(4), 3192-3203.
[37] Brockherde, F., Li, L., Burke, K., Müller, K.-R. By-passing the Kohn-Sham equations with machine learning. Nat. Commun., in press.
[38] Schütt, K. T., Kindermans, P. J., Sauceda, H. E., Chmiela, S., Tkatchenko, A., Müller, K. R. (2017). MolecuLeNet: A continuous-filter convolutional neural network for modeling quantum interactions. NIPS (accepted).

This two day workshop will bring together researchers from the various subdisciplines of Geometric Data Analysis, such as manifold learning, topological data analysis, shape analysis, will showcase recent progress in this field and will establish directions for future research. The focus will be on high dimensional and big data, and on mathematically founded methodology.


Specific aims
=============
One aim of this workshop is to build connections between Topological Data Analysis on one side and Manifold Learning on the other. This is starting to happen, after years of more or less separate evolution of the two fields. The moment has been reached when the mathematical, statistical and algorithmic foundations of both areas are mature enough -- it is now time to lay the foundations for joint topological and differential geometric understanding of data, and this workshop will expliecitly focus on this process.

The second aim is to bring GDA closer to real applications. We see the challenge of real problems and real data as a motivator for researchers to explore new research questions, to reframe and expand the existing theory, and to step out of their own sub-area. In particular, for people in GDA to see TDA and ML as one.

The impact of GDA in practice also depends on having scalable implementations of the most current results in theory. This workshop will showcase the GDA tools which achieve this and initiate a collective discussion about the tools that need to be built.

We intend this workshop to be a forum for researchers in all areas of Geometric Data Analysis. Trough the tutorials, we are reaching out to the wider NIPS audience, to the many potential users of of Geometric Data Analysis, to make them aware of the state of the art in GDA, and of the tools available. Last but not least, we hope that the scientists invited will bring these methods back to their communities.

The goal of the NIPS 2017 Machine Learning for Health Workshop (ML4H) is to foster collaborations that meaningfully impact medicine by bringing together clinicians, health data experts, and machine learning researchers. We aim to build on the success of the last two NIPS ML4H workshops which were widely attended and helped form the foundations of a new research community.

This year’s program emphasizes identifying previously unidentified problems in healthcare that the machine learning community hasn't addressed, or seeing old challenges through a new lens. While healthcare and medicine are often touted as prime examples for disruption by AI and machine learning, there has been vanishingly little evidence of this disruption to date. To interested parties who are outside of the medical establishment (e.g. machine learning researchers), the healthcare system can appear byzantine and impenetrable, which results in a high barrier to entry. In this workshop, we hope to reduce this activation energy by bringing together leaders at the forefront of both machine learning and healthcare for a dialog on areas of medicine that have immediate opportunities for machine learning. Attendees at this workshop will quickly gain an understanding of the key problems that are unique to healthcare and how machine learning can be applied to addressed these challenges.

The workshop will feature invited talks from leading voices in both medicine and machine learning. A key part of our workshop is the clinician pitch; a short presentation of open clinical problems where data-driven solutions can make an immediate difference. This year’s program will also include spotlight presentations and two poster sessions highlighting novel research contributions at the intersection of machine learning and healthcare. The workshop will conclude with an interactive a panel discussion where all speakers respond to questions provided by the audience.

A fundamental Challenge in neuroscience is to understand the elemental computations and algorithms by which brains perform information processing. This is of great significance to biologists, as well as, to engineers and computer scientists, who aim at developing energy efficient and intelligent solutions for the next generation of computers and autonomous devices. The benefits of collaborations between these fields are reciprocal, as brain-inspired computational algorithms and devices not only advance engineering, but also assist neuroscientists by conforming their models and making novel predictions. A large impediment toward such an efficient interaction is still the complexity of brains. We thus propose that the study of small model organisms should pioneer these efforts.

The nematode worm, C. elegans, provides a ready experimental system for reverse-engineering the nervous system, being one of the best studied animals in the life sciences. The neural connectome of C. elegans has been known for 30 years, providing the structural basis for building models of its neural information processing. Despite its small size, C. elegans exhibits complex behaviors, such as, locating food, mating partners and navigating its environment by integrating a plethora of environmental cues. Over the past years, the field has made an enormous progress in understanding some of the neural circuits that control sensory processing, decision making and locomotion. In laboratory, the crawling behavior of worms occurs mainly in 2D. This enables the use of machine learning tools to obtain quantitative behavioral descriptions of unprecedented accuracy. Moreover, neuronal imaging techniques have been developed so that the activity of nearly all nerve cells in the brain can be recorded in real time. Leveraging on these advancements, the community wide C. elegans OpenWorm project will make a realistic in silico simulation of a nervous system and the behavior it produces possible, for the first time. 

The goal of this workshop is to gather researchers in neuroscience and machine learning together, to advance understanding of the neural information processing of the worm and to outline what challenges still lie ahead. We particularly aim to:
- Comprehensively, introduce the nervous system of C. elegans. We will discuss the state-of-the-art findings and potential future solutions for modeling its neurons and synapses, complete networks of neurons and the various behaviors of the worm,
- Identify main challenges and their solutions in behavioral and neural data extraction, such as imaging techniques, generation of time series data from calcium imaging records and high resolution behavioral data, as well as cell recognition, cell tracking and image segmentation,
- Explore machine learning techniques for interpretation of brain data, such as time series analysis, feature extraction methods, complex network analysis, complex nonlinear systems analysis, large-scale parameter optimization methods, and representation learning,
- Get inspirations from this well-understood brain to design novel network architectures, control algorithms and neural processing units.
We have invited leading neuroscientists, machine learning scientists and interdisciplinary experts, to address these main objectives of the workshop, in the form of Keynote talks and a panel discussion. We also invite submissions of 4-page papers for posters, spotlight presentations and contributed talks, and offer travel awards.

Topics of interests are: Deep learning applications in nervous system data analysis, neural circuits analysis, behavior modeling, novel computational approaches and algorithms for brain data interpretations, brain simulation platforms, optimization algorithms for nonlinear systems, applications of machine learning methods to brain data and cell biology, complex network analysis, cell modeling, cell recognition and tracking, dynamic modeling of neural circuits and genetic regulatory networks.

The workshop’s webpage: https://sites.google.com/site/wwnip2017/

In recent years machine learning and causal inference have both seen important advances, especially through a dramatic expansion of their theoretical and practical domains. Machine learning has focused on ultra high-dimensional models and scalable stochastic algorithms, whereas causal inference has been guiding policy in complex domains involving economics, social and health sciences, and business. Through such advances a powerful cross-pollination has emerged as a new set of methodologies promising to deliver robust data analysis than each field could individually -- some examples include concepts such as doubly-robust methods, targeted learning, double machine learning, causal trees, all of which have recently been introduced.

This workshop is aimed at facilitating more interactions between researchers in machine learning and causal inference. In particular, it is an opportunity to bring together highly technical individuals who are strongly motivated by the practical importance and real-world impact of their work. Cultivating such interactions will lead to the development of theory, methodology, and - most importantly - practical tools, that better target causal questions across different domains.

In particular, we will highlight theory, algorithms and applications on automatic decision making systems, such as recommendation engines, medical decision systems and self-driving cars, as both producers and users of data. The challenge here is the feedback between learning from data and then taking actions that may affect what data will be made available for future learning. Learning algorithms have to reason about how changes to the system will affect future data, giving rise to challenging counterfactual and causal reasoning issues that the learning algorithm has to account for. Modern and scalable policy learning algorithms also require operating with non-experimental data, such as logged user interaction data where users click ads suggested by recommender systems trained on historical user clicks.

To further bring the community together around the use of such interaction data, this workshop will host a Kaggle challenge problem based on the first real-world dataset of logged contextual bandit feedback with non-uniform action-selection propensities. The dataset consists of several gigabytes of data from an ad placement system, which we have processed into multiple well-defined learning problems of increasing complexity, feedback signal, and context. Participants in the challenge problem will be able to discuss their results at the workshop.

Modern representation learning techniques like deep neural networks have had a major impact both within and beyond the field of machine learning, achieving new state-of-the-art performances with little or no feature engineering on a vast array of tasks. However, these gains are often difficult to translate into real-world settings as they require massive hand-labeled training sets. And in the vast majority of real-world settings, collecting such training sets by hand is infeasible due to the cost of labeling data or the paucity of data in a given domain (e.g. rare diseases in medical applications). In this workshop we focus on techniques for few sample learning and using weaker supervision when large unlabeled datasets are available, as well as theory associated with both.

One increasingly popular approach is to use weaker forms of supervision—i.e. supervision that is potentially noisier, biased, and/or less precise. An overarching goal of such approaches is to use domain knowledge and resources from subject matter experts, but to solicit it in higher-level, lower-fidelity, or more opportunistic ways. Examples include higher-level abstractions such as heuristic labeling rules, feature annotations, constraints, expected distributions, and generalized expectation criteria; noisier or biased labels from distant supervision, crowd workers, and weak classifiers; data augmentation strategies to express class invariances; and potentially mismatched training data such as in multitask and transfer learning settings.

Along with practical methods and techniques for dealing with limited labeled data settings, this workshop will also focus on the theory of learning in this general setting. Although several classic techniques in the statistical learning theory exist which handle the case of few samples and high dimensions, extending these results for example to the recent success of deep learning is still a challenge. How can the theory or the techniques that have gained success in deep learning be adapted to the case of limited labeled data? How can systems designed (and potentially deployed) for large scale learning be adapted to small data settings? What are efficient and practical ways to incorporate prior knowledge?

This workshop will focus on highlighting both practical and theoretical aspects of learning with limited labeled data, including but not limited to topics such as:
- Learning from noisy labels
- “Distant” or heuristic supervision
- Non-standard labels such as feature annotations, distributions, and constraints
-Data augmentation and/or the use of simulated data
- Frameworks that can tackle both very few samples and settings with more data without
extensive intervention.
- Effective and practical techniques for incorporating domain knowledge
- Applications of machine learning for small data problems in medical images and industry

Scope

'Medical Imaging meets NIPS' is a satellite workshop at NIPS 2017. The workshop aims to bring researchers together from the medical image computing and machine learning community. The objective is to discuss the major challenges in the field and opportunities for joining forces. The event will feature a series of high-profile invited speakers from industry, academia, engineering and medical sciences who aim to give an overview of recent advances, challenges, latest technology and efforts for sharing clinical data.

Motivation

Medical imaging is facing a major crisis with an ever increasing complexity and volume of data and immense economic pressure. The interpretation of medical images pushes human abilities to the limit with the risk that critical patterns of disease go undetected. Machine learning has emerged as a key technology for developing novel tools in computer aided diagnosis, therapy and intervention. Still, progress is slow compared to other fields of visual recognition which is mainly due to the domain complexity and constraints in clinical applications which require most robust, accurate, and reliable solutions.

Call for Abstracts

We invite submissions of extended abstracts for poster presentation during the workshop. Submitting an abstract is an ideal way of engaging with the workshop and to showcase research in the area of machine learning for medical imaging. Submitted work does not have to be original and can be already published elsewhere and/or can be of preliminary nature. There will be no workshop proceedings, and the poster session may be conditional on receiving sufficiently many submissions. Accepted abstracts together with author information will be made available on this website.

Dates

Submissions: Sunday, October 29th, midnight PST
Notifications: Sunday, November 5th
Workshop: Saturday, December 9th, 8:45 AM - 6 PM

Schedule (tentative)

Every year hundreds of papers are published at NIPS. Although the authors provide sound and scientific description and proof of their ideas, there is no space for explaining all the tricks and details that can make the implementation of the paper work. The goal of this workshop is to help authors evangelize their paper to the industry and expose the participants to all the Machine Learning/Artificial Intelligence know-how that cannot be found in the papers. Also the effect/importance of tuning parameters is rarely discussed, due to lack of space.
Submissions
We encourage you to prepare a poster of your favorite paper that explains graphically and at a higher level the concepts and the ideas discussed in it. You should also submit a jupyter notebook that explains in detail how equations in the paper translate to code. You are welcome to use any of the famous platforms like tensorFlow, Keras, MxNet, CNTK, etc.
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For more information https://www.mltrain.cc/

The goal of this workshop is to bring together cognitive scientists, neuroscientists, and AI researchers to discuss opportunities for improving machine learning by leveraging our scientific understanding of human perception and cognition. There is a history of making these connections: artificial neural networks were originally motivated by the massively parallel, deep architecture of the brain; considerations of biological plausibility have driven the development of learning procedures; and architectures for computer vision draw parallels to the connectivity and physiology of mammalian visual cortex. However, beyond these celebrated examples, cognitive science and neuroscience has fallen short of its potential to influence the next generation of AI systems. Areas such as memory, attention, and development have rich theoretical and experimental histories, yet these concepts, as applied to AI systems so far, only bear a superficial resemblance to their biological counterparts.

The premise of this workshop is that there are valuable data and models from cognitive science that can inform the development of intelligent adaptive machines, and can endow learning architectures with the strength and flexibility of the human cognitive architecture. The structures and mechanisms of the mind and brain can provide the sort of strong inductive bias needed for machine-learning systems to attain human-like performance. We conjecture that this inductive bias will become more important as researchers move from domain-specific tasks such as object and speech recognition toward tackling general intelligence and the human-like ability to dynamically reconfigure cognition in service of changing goals. For ML researchers, the workshop will provide access to a wealth of data and concepts situated in the context of contemporary ML. For cognitive scientists, the workshop will suggest research questions that are of critical interest to ML researchers.

The workshop will focus on three interconnected topics of particular relevance to ML:

(1) Learning and development. Cognitive capabilities expressed early in a child’s development are likely to be crucial for bootstrapping adult learning and intelligence. Intuitive physics and intuitive psychology allow the developing organism to build an understanding of the world and of other agents. Additionally, children and adults often demonstrate “learning-to-learn,” where previous concepts and skills form a compositional basis for learning new concepts and skills.

(2) Memory. Human memory operates on multiple time scales, from memories that literally persist for the blink of an eye to those that persist for a lifetime. These different forms of memory serve different computational purposes. Although forgetting is typically thought of as a disadvantage, the ability to selectively forget/override irrelevant knowledge in nonstationary environments is highly desirable.

(3) Attention and Decision Making. These refer to relatively high-level cognitive functions that allow task demands to purposefully control an agent’s external environment and sensory data stream, dynamically reconfigure internal representation and architecture, and devise action plans that strategically trade off multiple, oft-conflicting behavioral objectives.

The long-term aims of this workshop are:

* to promote work that incorporates insights from human cognition to suggest novel and improved AI architectures;

* to facilitate the development of ML methods that can better predict human behavior; and

* to support the development of a field of ‘cognitive computing’ that is more than a marketing slogan一a field that improves on both natural and artificial cognition by synergistically advancing each and integrating their strengths in complementary manners.

The past five years have seen a huge increase in the capabilities of deep neural networks. Maintaining this rate of progress however, faces some steep challenges, and awaits fundamental insights. As our models become more complex, and venture into areas such as unsupervised learning or reinforcement learning, designing improvements becomes more laborious, and success can be brittle and hard to transfer to new settings.

This workshop seeks to highlight recent works that use theory as well as systematic experiments to isolate the fundamental questions that need to be addressed in deep learning. These have helped flesh out core questions on topics such as generalization, adversarial robustness, large batch training, generative adversarial nets, and optimization, and point towards elements of the theory of deep learning that is expected to emerge in the future.

The workshop aims to enhance this confluence of theory and practice, highlighting influential work with these methods, future open directions, and core fundamental problems. There will be an emphasis on discussion, via panels and round tables, to identify future research directions that are promising and tractable.

Challenges in machine learning and data science are competitions running over several weeks or months to resolve problems using provided datasets or simulated environments. The playful nature of challenges naturally attracts students, making challenge a great teaching resource. For this fourth edition of the CiML workshop at NIPS we want to explore the impact of machine learning challenges as a research tool. The workshop will give a large part to discussions around several axes: (1) benefits and limitations of challenges as a research tool; (2) methods to induce and train young researchers; (3) experimental design to foster contributions that will push the state of the art.
CiML is a forum that brings together workshop organizers, platform providers, and participants to discuss best practices in challenge organization and new methods and application opportunities to design high impact challenges. Following the success of last year's workshop, in which a fruitful exchange led to many innovations, we propose to reconvene and discuss new opportunities for challenges as a research tool, one of the hottest topics identified in last year's discussions. We have invited prominent speakers in this field.
We will also reserve time to an open discussion to dig into other topic including open innovation, collaborative challenges (coopetitions), platform interoperability, and tool mutualisation.
The audience of this workshop is targeted to workshop organizers, participants, and anyone with scientific problem involving machine learning, which may be formulated as a challenge. The emphasis of the workshop is on challenge design. Hence it complements nicely the workshop on the NIPS 2017 competition track and will help paving the way toward next year's competition program.

In order to be helpful to users and to society at large, an autonomous agent needs to be aligned with the objectives of its stakeholders. Misaligned incentives are a common and crucial problem with human agents --- we should expect similar challenges to arise from misaligned incentives with artificial agents. For example, it is not uncommon to see reinforcement learning agents ‘hack’ their specified reward function. How do we build learning systems that will reliably achieve a user's intended objective? How can we ensure that autonomous agents behave reliably in unforeseen situations? How do we design systems whose behavior will be aligned with the values and goals of society at large? As AI capabilities develop, it is crucial for the AI community to come to satisfying and trustworthy answers to these questions. This workshop will focus on three central challenges in value alignment: learning complex rewards that reflect human preferences (e.g. meaningful oversight, preference elicitation, inverse reinforcement learning, learning from demonstration or feedback), engineering reliable AI systems (e.g. robustness to distributional shift, model misspecification, or adversarial data, via methods such as adversarial training, KWIK-style learning, or transparency to human inspection), and dealing with bounded rationality and incomplete information in both AI systems and their users (e.g. acting on incomplete task specifications, learning from users who sometimes make mistakes). We also welcome submissions that do not directly fit these categories but generally deal with problems relating to value alignment in artificial intelligence.

Five years ago, it took more than a month to train a state-of-the-art image recognition model on the ImageNet dataset. Earlier this year, Facebook demonstrated that such a model could be trained in an hour. However, if we could parallelize this training problem across the world’s fastest supercomputers (~100 PFlops), it would be possible to train the same model in under a minute. This workshop is about closing that gap: how can we turn months into minutes and increase the productivity of machine learning researchers everywhere?

This one-day workshop will facilitate active debate and interaction across many different disciplines. The conversation will range from algorithms to infrastructure to silicon, with invited speakers from Cerebras, DeepMind, Facebook, Google, OpenAI, and other organizations. When should synchronous training be preferred over asynchronous training? Are large batch sizes the key to reach supercomputer scale, or is it possible to fully utilize a supercomputer at batch size one? How important is sparsity in enabling us to scale? Should sparsity patterns be structured or unstructured? To what extent do we expect to customize model architectures for particular problem domains, and to what extent can a “single model architecture” deliver state-of-the-art results across many different domains? How can new hardware architectures unlock even higher real-world training performance?

Our goal is bring people who are trying to answer any of these questions together in hopes that cross pollination will accelerate progress towards deep learning at true supercomputer scale.

Our transportation systems are poised for a transformation as we make progress on autonomous vehicles, vehicle-to-vehicle (V2V) and vehicle-to-everything (V2X) communication infrastructures, and smart road infrastructures such as smart traffic lights.
There are many challenges in transforming our current transportation systems to the future vision. For example, how to make perception accurate and robust to accomplish safe autonomous driving? How to learn long term driving strategies (known as driving policies) so that autonomous vehicles can be equipped with adaptive human negotiation skills when merging, overtaking and giving way, etc? how do we achieve near-zero fatality? How do we optimize efficiency through intelligent traffic management and control of fleets? How do we optimize for traffic capacity during rush hours? To meet these requirements in safety, efficiency, control, and capacity, the systems must be automated with intelligent decision making.

Machine learning will be essential to enable intelligent transportation systems. Machine learning has made rapid progress in self-driving, e.g. real-time perception and prediction of traffic scenes, and has started to be applied to ride-sharing platforms such as Uber (e.g. demand forecasting) and crowd-sourced video scene analysis companies such as Nexar (understanding and avoiding accidents). To address the challenges arising in our future transportation system such as traffic management and safety, we need to consider the transportation systems as a whole rather than solving problems in isolation. New machine learning solutions are needed as transportation places specific requirements such as extremely low tolerance on uncertainty and the need to intelligently coordinate self-driving cars through V2V and V2X.

The goal of this workshop is to bring together researchers and practitioners from all areas of intelligent transportations systems to address core challenges with machine learning. These challenges include, but are not limited to
accurate and efficient pedestrian detection, pedestrian intent detection,
machine learning for object tracking,
unsupervised representation learning for autonomous driving,
deep reinforcement learning for learning driving policies,
cross-modal and simulator to real-world transfer learning,
scene classification, real-time perception and prediction of traffic scenes,
uncertainty propagation in deep neural networks,
efficient inference with deep neural networks
predictive modeling of risk and accidents through telematics, modeling, simulation and forecast of demand and mobility patterns in large scale urban transportation systems,
machine learning approaches for control and coordination of traffic leveraging V2V and V2X infrastructures,

The workshop will include invited speakers, panels, presentations of accepted papers and posters. We invite papers in the form of short, long and position papers to address the core challenges mentioned above. We encourage researchers and practitioners on self-driving cars, transportation systems and ride-sharing platforms to participate. Since this is a topic of broad and current interest, we expect at least 150 participants from leading university researchers, auto-companies and ride-sharing companies.

Deep Machine Learning has changed the computing paradigm. Products of today are built with machine intelligence as a central attribute, and consumers are beginning to expect near-human interaction with the appliances they use. However, much of the Deep Learning revolution has been limited to the cloud, enabled by popular toolkits such as Caffe, TensorFlow, and MxNet, and by specialized hardware such as TPUs. In comparison, mobile devices until recently were just not fast enough, there were limited developer tools, and there were limited use cases that required on-device machine learning. That has recently started to change, with the advances in real-time computer vision and spoken language understanding driving real innovation in intelligent mobile applications. Several mobile-optimized neural network libraries were recently announced (CoreML [1], Caffe2 for mobile [2], TensorFlow Lite [3]), which aim to dramatically reduce the barrier to entry for mobile machine learning. Innovation and competition at the silicon layer has enabled new possibilities for hardware acceleration. To make things even better, mobile-optimized versions of several state-of-the-art benchmark models were recently open sourced [4]. Widespread increase in availability of connected “smart” appliances for consumers and IoT platforms for industrial use cases means that there is an ever-expanding surface area for mobile intelligence and ambient devices in homes. All of these advances in combination imply that we are likely at the cusp of a rapid increase in research interest in on-device machine learning, and in particular, on-device neural computing.

Significant research challenges remain, however. Mobile devices are even more personal than “personal computers” were. Enabling machine learning while simultaneously preserving user trust requires ongoing advances in the research of differential privacy and federated learning techniques. On-device ML has to keep model size and power usage low while simultaneously optimizing for accuracy. There are a few exciting novel approaches recently being developed in mobile optimization of neural networks. Lastly, the newly prevalent use of camera and voice as interaction models has fueled exciting research towards neural techniques for image and speech/language understanding.

With this emerging interest as well as the wealth of challenging research problems in mind, we are proposing the first NIPS workshop dedicated to on-device machine learning for mobile and ambient home consumer devices. We believe that interest in this space is only going to increase, and we hope that the workshop plays the role of an influential catalyst to foster research and collaboration in this nascent community.

The next wave of ML applications will have significant processing on mobile and ambient devices. Some immediate examples of these are single-image depth estimation, object recognition and segmentation running on-device for creative effects, or on-device recommender and ranking systems for privacy-preserving, low-latency experiences. This workshop will bring ML practitioners up to speed on the latest trends for on-device applications of ML, offer an overview of the latest HW and SW framework developments, and champion active research towards hard technical challenges emerging in this nascent area. The target audience for the workshop is both industrial and academic researchers and practitioners of on-device, native machine learning. The workshop will cover both “informational” and “aspirational” aspects of this emerging research area for delivering ground-breaking experiences on real-world products.


[1] https://developer.apple.com/machine-learning/
[2] https://caffe2.ai/
[3] https://www.tensorflow.org/mobile/
[4] https://opensource.googleblog.com/2017/06/mobilenets-open-source-models-for.html

Machine learning has become an indispensable tool for a number of tasks ranging from the detection of objects in images to the understanding of natural languages. While these models reach impressively high predictive accuracy, they are often perceived as black-boxes, and it is not clear what information in the input data is used for predicting. In sensitive applications such as medical diagnosis or self-driving cars, where a single incorrect prediction can be very costly, the reliance of the model on the right features must be guaranteed. This indeed lowers the risk that the model behaves erroneously in presence of novel factors of variation in the test data. Furthermore, interpretability is instrumental when applying machine learning to the sciences, as the detailed understanding of the trained model (e.g., what features it uses to capture the complex relations between physical or biological variables) is a prerequisite for building meaningful new scientific hypotheses. Without such understanding and the possibility of verification that the model has learned something meaningful (e.g. obeying the known physical or biological laws), even the best predictor is of no use for scientific purposes. Finally, also from the perspective of a deep learning engineer, being able to visualize what the model has (or has not) learned is valuable as it allows to improve current models by e.g. identifying biases in the data or the training procedure, or by comparing the strengths and weaknesses of different architectures.

Not surprisingly, the problem of visualizing and understanding neural networks has recently received a lot of attention in the community. Various techniques for interpreting deep neural networks have been proposed and several workshops have been organized on related topics. However, the theoretical foundations of the interpretability problem are yet to be investigated and the usefulness of the proposed methods in practice still needs to be demonstrated.

Our NIPS 2017 Workshop “Interpreting, Explaining and Visualizing Deep Learning – Now what?” aims to review recent techniques and establish new theoretical foundations for interpreting and understanding deep learning models. However, it will not stop at the methodological level, but also address the “now what?” question. This strong focus on the applications of interpretable methods in deep learning distinguishes this workshop from previous events as we aim to take the next step by exploring and extending the practical usefulness of Interpreting, Explaining and Visualizing in Deep Learning. Also with this workshop we aim to identify new fields of applications for interpretable deep learning. Since the workshop will host invited speakers from various application domains (computer vision, NLP, neuroscience, medicine), it will provide an opportunity for participants to learn from each other and initiate new interdisciplinary collaborations. The workshop will contain invited research talks, short methods and applications talks, a poster and demonstration session and a panel discussion. A selection of accepted papers together with the invited contributions will be published in an edited book by Springer LNCS in order to provide a representative overview of recent activities in this emerging research field.

An important facet of human experience is our ability to break down what we observe and interact with, along characteristic lines. Visual scenes consist of separate objects, which may have different poses and identities within their category. In natural language, the syntax and semantics of a sentence can often be separated from one another. In planning and cognition plans can be broken down into immediate and long term goals. Inspired by this much research in deep representation learning has gone into finding disentangled factors of variation. However, this research often lacks a clear definition of what disentangling is or much relation to work in other branches of machine learning, neuroscience or cognitive science. In this workshop we intend to bring a wide swathe of scientists studying disentangled representations under one roof to try to come to a unified view of the problem of disentangling.

The workshop will address these issues through 3 focuses:
What is disentangling: Are disentangled representations just the same as statistically independent representations, or is there something more? How does disentangling relate to interpretability? Can we define what it means to separate style and content, or is human judgement the final arbiter? Are disentangled representations the same as equivariant representations?
How can disentangled representations be discovered: What is the current state of the art in learning disentangled representations? What are the cognitive and neural underpinnings of disentangled representations in animals and humans? Most work in disentangling has focused on perception, but we will encourage dialogue with researchers in natural language processing and reinforcement learning as well as neuroscientists and cognitive scientists.
Why do we care about disentangling: What are the downstream tasks that can benefit from using disentangled representations? Does the downstream task define the relevance of the disentanglement to learn? What does disentangling get us in terms of improved prediction or behavior in intelligent agents?

Datasets in neuroscience are increasing in size at alarming rates relative to our ability to analyze them. This workshop aims at discussing new frameworks for processing and making sense of large neural datasets.

The morning session will focus on approaches for processing large neuroscience datasets. Examples include: distributed + high-performance computing, GPU and other hardware accelerations, spatial databases and other compression schemes used for large neuroimaging datasets, online machine learning approaches for handling large data sizes, randomization and stochastic optimization.

The afternoon session will focus on abstractions for modelling large neuroscience datasets. Examples include graphs, graphical models, manifolds, mixture models, latent variable models, spatial models, and factor learning.

In addition to talks and discussions, we plan to have papers submitted and peer reviewed. Workshop “proceedings” will consist of links to unpublished arXiv or bioarXiv papers that are of exceptional quality and are well aligned with the workshop scope. Some accepted papers will also be invited for an oral presentation; the remaining authors will be invited to present a poster.

Many algorithms in machine learning, computer vision, physical simulation, and other fields require the calculation of gradients and other derivatives. Manual derivation of gradients can be time consuming and error-prone. Automatic differentiation comprises a set of techniques to calculate the derivative of a numerical computation expressed as a computer program. These techniques are commonly used in atmospheric sciences and computational fluid dynamics, and have more recently also been adopted by machine learning researchers.

Practitioners across many fields have built a wide set of automatic differentiation tools, using different programming languages, computational primitives and intermediate compiler representations. Each of these choices comes with positive and negative trade-offs, in terms of their usability, flexibility and performance in specific domains.

This workshop will bring together researchers in the fields of automatic differentiation and machine learning to discuss ways in which advanced automatic differentiation frameworks and techniques can enable more advanced machine learning models, run large-scale machine learning on accelerators with better performance, and increase the usability of machine learning frameworks for practitioners. Topics for discussion will include:

* What abstractions (languages, kernels, interfaces, instruction sets) do we need to develop advanced automatic differentiation frameworks for the machine learning ecosystem?
* What different use cases exist in machine learning, from large-scale performance-critical models to small prototypes, and how should our toolsets reflect these needs?
* What advanced techniques from the automatic differentiation literature, such as checkpointing, differentiating through iterative processes or chaotic systems, cross-country elimination, etc., could be adopted by the ML community to enable research on new models?
* How can we foster greater collaboration between the fields of machine learning and automatic differentiation?

Social Media and other online media sources play a critical role in distributing news and informing public opinion. Initially it seemed that democratising the dissemination of information and news with online media might be wholly good – but during the last year we have witnessed other perhaps less positive effects.
The algorithms that prioritise content for users aim to provide information that will be ‘liked’ by each user in order to retain their attention and interest. These algorithms are now well-tuned and are indeed able to match content to different users’ preferences. This has meant that users increasingly see content that aligns with their world view, confirms their beliefs, supports their opinions, in short that maintains their ‘information bubble’, creating the so-called echo-chambers. As a result, views have often become more polarised rather than less, with people expressing genuine disbelief that fellow citizens could possibly countenance alternative opinions, be they pro- or anti-brexit, pro- or anti-Trump. Perhaps the most extreme example is that of fake news in which news is created in order to satisfy and reinforce certain beliefs.
This polarisation of views cannot be beneficial for society. As the success of Computer Science and more specifically Machine Learning have led to this undesirable situation, it is natural that we should now ask how Online Content might be prioritised in such a way that users are still satisfied with an outlet but at the same time are not led to more extreme and polarised opinions.
What is the effect of content prioritisation – and more generally, the effect of the affordances of the social network – on the nature of discussion and debate? Social networks could potentially enable society-wide debate and collective intelligence. On the other hand, they could also encourage communal reinforcement by enforcing conformity within friendship groups, in that it is a daring person who posts an opinion at odds with the majority of their friends. Each design of content prioritisation may nudge users towards particular styles of both content-viewing and of content-posting and discussion. What is the nature of the interaction between content-presentation and users’ viewing and debate?
Content may be prioritised either ‘transparently’ according to users’ explicit choices of what they want to see, combined with transparent community voting, and moderators whose decisions can be questioned (e.g. Reddit). At the other extreme, content may be prioritised by proprietary algorithms that model each user’s preferences and then predict what they want to see. What is the range of possible designs and what are their effects? Could one design intelligent power-tools for moderators?
The online portal Reddit is a rare exception to the general rule in that it has proven a popular site despite employing a more nuanced algorithm for the prioritisation of content. The approach was, however, apparently designed to manage traffic flows rather than create a better balance of opinions. It would, therefore, appear that even for this algorithm its effect on prioritisation is only partially understood or intended.
If we view social networks as implementing a large scale message-passing algorithm attempting to perform inference about the state of the world and possible interventions and/or improvements, the current prioritisation algorithms create many (typically short) cycles. It is well known that inference based on message passing fails to converge to an optimal solution if the underlying graph contains cycles because information then becomes incorrectly weighted. Perhaps a similar situation is occurring with the use of social media? Is it possible to model this phenomenon as an approximate inference task?
The workshop will provide a forum for the presentation and discussion of analyses of online prioritisation with emphasis on the biases that such prioritisations introduce and reinforce. Particular interest will be placed on presentations that consider alternative ways of prioritising content where it can be argued that they will reduce the negative side-effects of current methods while maintaining user loyalty.

Call for contributions - see conference web page via link above.

We will issue a call for contributions highlighting but not restricted to the following themes:
() predicting future global events from media
() detecting and predicting new major trends in the scientific literature
() enhancing content with information from fact checkers
() detection of fake news
() detecting and mitigating tribalism among online personas
() adapted and improved mechanisms of information spreading
(*) algorithmic fairness in machine learning

Bayesian optimization (BO) is a recent subfield of machine learning comprising a collection of methodologies for the efficient optimization of expensive black-box functions. BO techniques work by fitting a model to black-box function data and then using the model's predictions to decide where to collect data next, so that the optimization problem can be solved using only a small number of function evaluations. The resulting methods are characterized by their high sample-efficiency when compared to alternative black-box optimization algorithms, enabling the solution of new challenging problems. For example, in recent years, BO has become a popular tool in the machine learning community for the excellent performance attained in the problem of hyperparameter tuning, with important results both in academia and industry. This success has made BO a crucial player in the current trend of “automatic machine learning”.

As new BO methods have been developed, the area of applicability has been continuously expanding. While the problem of hyperparameter tuning permeates all disciplines, the field has moved towards more specific problems in science and engineering requiring of new advanced methodology. Today, Bayesian optimization is the most promising approach for accelerating and automating science and engineering. Therefore, we have chosen this year's theme for the workshop to be "Bayesian optimization for science and engineering".

We enumerate below a few of the recent directions in which BO methodology is being pushed forward to address specific problems in science and engineering:

-Beyond Gaussian processes. While GPs are the default choice in BO, specific problems in science and engineering require to collect larger amounts of complex data. In these settings, it is necessary to use alternative models to capture complex patterns with higher accuracy. For example, Bayesian neural networks or deep Gaussian processes. How can we do efficient BO with these type of models?

-Optimization in structured domains. Many problems in science and engineering require to perform optimization in complex spaces which are different from the typical box-constrained subset of the real coordinate space. For example, how can we efficiently optimize over graphs, discrete sequences, trees, computer programmes, etc.?

-Safe optimization. Critical applications in science and engineering may include configurations in the search space that may be unsafe or harmful and may lead to system failure. How can we perform efficient BO while avoiding these unsafe settings?

-Incorporating domain specific knowledge. In many optimization settings there is available a lot of domain specific information that can be used to build bespoke BO methods with significantly better performance than its off-the-shelf counterparts. How can we easily encode and transfer available knowledge into BO methods in an easy and fast manner?

-Optimization with structured output response. In specific cases, each black-box may produce additional output values besides an estimate of the objective function. For example, when tuning a simulator, besides the final output value, we may also obtain structured data related to the execution trace of the simulator. How can design BO methods that automatically exploit this extra structured output?

-Scalability and fast evaluations. Recent problems require to collect a massive amount of data in parallel and at cost that is usually lower than in typical BO problems. How can we design efficient BO methods that collect very large batches of data in parallel? How can we automatically adjust the cost of BO methods so that they are efficient even when the data collection is not highly expensive?

The target audience for this workshop consists of both industrial and academic practitioners of Bayesian optimization as well as researchers working on theoretical and practical advances in model based optimization across different engineering areas. We expect that this pairing of theoretical and applied knowledge will lead to an interesting exchange of ideas and stimulate an open discussion about the long term goals and challenges of the Bayesian optimization community.

The main goal of the workshop is to serve as a forum of discussion and to encourage collaboration between the diverse set of scientist that develop and use Bayesian optimization and related techniques. Researchers and practitioners in Academia are welcome, as well people form the wider optimization, engineering and probabilistic modeling communities.

Communication is one of the most impressive human abilities. The question of how communication arises has been studied for many decades, if not centuries. However, due to the computational and representational limitations, in the past problem-settings had to be restricted to low dimensional, simple observation spaces. With the rise of deep reinforcement learning methods, this question can now be studied in complex multi-agent settings, which has lead to flourishing activity in the area over the last two years. In these settings agents can learn to communicate in grounded multi-modal environments and rich communication protocols emerge.

However, the recent research has been largely disconnected from the study of emergent communication in other fields and even from work done on this topic in previous decades. This workshop will provide a forum for a variety of researchers from different fields (machine learning, game-theory, linguistics, cognitive science, and programming languages) interested in the question of communication and emergent language to exchange ideas.

https://sites.google.com/site/emecom2017/

For many in the sciences, collaboration is a given, or at least a given assumption. The field of AIML is no different, and collaboration across fields and disciplines has long been a source of data and funding. But for many, effective collaboration can be confounding, and for those who have never worked with someone from a different field, it can be confusing and daunting.

Good collaboration requires good communication, but more fundamentally, clear communication is a core skillset for anyone. It takes practice, and in highly specialized fields, it is often subject to an all-too-common malady: the curse of knowledge. The curse of knowledge happens when experts in a field, communicating within their field, begin to make assumptions about the knowledge and understanding of their audience and begin to overlook the fundamentals of clear communication. They do this because for an audience of their peers, they seem to become less necessary, while short cuts like jargon seem to make communication faster and more efficient. But today, clear communication around issues and techniques in machine intelligence work is crucial not only within the community, but also to foster collaboaration across disciplines, and between the community and the lay public.

In this workshop we will explore stories of success in both of these topics through a series of short talks by pairs of collaborators followed by a panel discussion. We will then apply some of the principles from these areas by exploring practical models and skills to aid in both.

We intend to work with partners to organize attendees at the beginning of the workshop to create real opportunities for good collaborations among them and to inspire them to consider how they might work together.

Attendees will leave with resources to help them more effectively collaborate and to help them organize and communicate about their own work.

The field of computational biology has seen dramatic growth over the past few years. A wide range of high-throughput technologies developed in the last decade now enable us to measure parts of a biological system at various resolutions—at the genome, epigenome, transcriptome, and proteome levels. These technologies are now being used to collect data for an ever-increasingly diverse set of problems, ranging from classical problems such as predicting differentially regulated genes between time points and predicting subcellular localization of RNA and proteins, to models that explore complex mechanistic hypotheses bridging the gap between genetics and disease, population genetics and transcriptional regulation. Fully realizing the scientific and clinical potential of these data requires developing novel supervised and unsupervised learning methods that are scalable, can accommodate heterogeneity, are robust to systematic noise and confounding factors, and provide mechanistic insights.

The goals of this workshop are to i) present emerging problems and innovative machine learning techniques in computational biology, and ii) generate discussion on how to best model the intricacies of biological data and synthesize and interpret results in light of the current work in the field. We will invite several leaders at the intersection of computational biology and machine learning who will present current research problems in computational biology and lead these discussions based on their own research and experiences. We will also have the usual rigorous screening of contributed talks on novel learning approaches in computational biology. We encourage contributions describing either progress on new bioinformatics problems or work on established problems using methods that are substantially different from established alternatives. Deep learning, kernel methods, graphical models, feature selection, non-parametric models and other techniques applied to relevant bioinformatics problems would all be appropriate for the workshop. We will also encourage contributions to address new challenges in analyzing data generated from gene editing, single cell genomics and other novel technologies. The targeted audience are people with interest in machine learning and applications to relevant problems from the life sciences, including NIPS participants without any existing research link to computational biology. Many of the talks will be of interest to the broad machine learning community.

Optimal transport (OT) is gradually establishing itself as a powerful and essential tool to compare probability measures, which in machine learning take the form of point clouds, histograms, bags-of-features, or more generally datasets to be compared with probability densities and generative models. OT can be traced back to early work by Monge, and later to Kantorovich and Dantzig during the birth of linear programming. The mathematical theory of OT has produced several important developments since the 90's, crowned by Cédric Villani's Fields Medal in 2010. OT is now transitioning into more applied spheres, including recent applications to machine learning, because it can tackle challenging learning scenarios including dimensionality reduction, structured prediction problems that involve histograms, and estimation of generative models in highly degenerate, high-dimensional problems. This workshop will follow that organized 3 years ago (NIPS 2014) and will seek to amplify that trend. We will provide the audience with an update on all of the very recent successes brought forward by efficient solvers and innovative applications through a long list of invited talks. We will add to that a few contributed presentations (oral, and, if needed posters) and, finally, a panel for all invited speakers to take questions from the audience and formulate more nuanced opinions on this nascent field.

This workshop focuses on “machine teaching”, the inverse problem of machine learning, in which the goal is to find an optimal training set given a machine learning algorithm and a target model. The study of machine teaching began in the early 1990s, primarily coming out of computational learning theory. Recently, there has been a surge of interest in machine teaching as several different communities within machine learning have found connections to this problem; these connections have included the following:

* machine teaching has close connections to newly introduced models of interaction in machine learning community, such as curriculum learning, self-paced learning, and knowledge distillation. [Hinton et al. 2015; Bengio et al. 2009]

* there are strong theoretical connections between the Teaching-dimension (the sample complexity of teaching) and the VC-dimension (the sample complexity of learning from randomly chosen examples). [Doliwa et al. 2014]

* machine teaching problem formulation has been recently studied in the context of diverse applications including personalized educational systems, cyber-security problems, robotics, program synthesis, human-in-the-loop systems, and crowdsourcing. [Jha et al. 2016; Zhu 2015; Mei & Zhu 2015; Ba & Caruana 2014; Patil et al. 2014; Singla et al. 2014; Cakmak & Thomaz 2014]

In this workshop, we draw attention to machine teaching by emphasizing how the area of machine teaching interacts with emerging research trends and application domains relevant to the NIPS community. The goal of this workshop is to foster these ideas by bringing together researchers with expertise/interest in the inter-related areas of machine teaching, interactive machine learning, robotics, cyber-security problems, generative adversarial networks, educational technologies, and cognitive science.

Topics of interests in the workshop include (but are not limited to):

* Theoretical foundations of machine teaching:
* using tools from information theory to develop better mathematical models of teaching;
* characterizing the complexity of teaching when a teacher has limited power, or incomplete knowledge of student’s model, or a mismatch in feature representations;
* algorithms for adaptive teaching by interactively inferring the learner’s state;
* new notions of Teaching-dimension for generic teaching settings.

* Connections to machine learning models:
* the information complexity of teaching and query complexity;
* machine teaching vs. curriculum learning and other models of interactive machine learning;
* teaching reinforcement learning agents.

Applications of machine teaching to adversarial attacks, including cyber-security problems, generative adversarial networks, attacks on machine learning algorithms, etc.

* Applications of machine teaching to educational technologies:
* using the machine teaching formulation to enable more rigorous and generalizable approaches for developing intelligent tutoring systems;
* behavioral experiments to identify good cognitive models of human learning processes.

* Novel applications for machine teaching such as program synthesis, human-robot interactions, social robotics, etc.

Industry-wide successes of machine learning at the dawn of the (so-called) big data era has led to an increasing gap between practitioners and theoreticians. The former are using off-the-shelf statistical and machine learning methods, while the latter are designing and studying the mathematical properties of such algorithms. The tradeoff between those two movements is somewhat addressed by Bayesian researchers, where sound mathematical guarantees often meet efficient implementation and provide model selection criteria. In the late 90s, a new paradigm has emerged in the statistical learning community, used to derive probably approximately correct (PAC) bounds on Bayesian-flavored estimators. This PAC-Bayesian theory has been pioneered by Shawe-Taylor and Willamson (1997), and McAllester (1998, 1999). It has been extensively formalized by Catoni (2004, 2007) and has triggered, slowly but surely, increasing research efforts during last decades.

We believe it is time to pinpoint the current PAC-Bayesian trends relatively to other modern approaches in the (statistical) machine learning community. Indeed, we observe that, while the field grows by its own, it took some undesirable distance from some related areas. Firstly, it seems to us that the relation to Bayesian methods has been forsaken in numerous works, despite the potential of PAC-Bayesian theory to bring new insights to the Bayesian community and to go beyond the classical Bayesian/frequentist divide. Secondly, the PAC-Bayesian methods share similarities with other quasi-Bayesian (or pseudo-Bayesian) methods studying Bayesian practices from a frequentist standpoint, such as the Minimum Description Length (MDL) principle (Grünwald, 2007). Last but not least, even if some practical and theory grounded learning algorithm has emerged from PAC-Bayesian works, these are almost unused for real-world problems.

In short, this workshop aims at gathering statisticians and machine learning researchers to discuss current trends and the future of {PAC,quasi}-Bayesian learning. From a broader perspective, we aim to bridge the gap between several communities that can all benefit from sharper statistical guarantees and sound theory-driven learning algorithms.

References
[1] J. Shawe-Taylor and R. Williamson. A PAC analysis of a Bayes estimator. In Proceedings of COLT, 1997.
[2] D. A. McAllester. Some PAC-Bayesian theorems. In Proceedings of COLT, 1998.
[3] D. A. McAllester. PAC-Bayesian model averaging. In Proceedings of COLT, 1999.
[4] O. Catoni. Statistical Learning Theory and Stochastic Optimization. Saint-Flour Summer School on Probability Theory 2001 (Jean Picard ed.), Lecture Notes in Mathematics. Springer, 2004.
[5] O. Catoni. PAC-Bayesian supervised classification: the thermodynamics of statistical learning. Institute of Mathematical Statistics Lecture Notes—Monograph Series, 56. Institute of Mathematical Statistics, 2007.
[6] P. D. Grünwald. The Minimum Description Length Principle. The MIT Press, 2007.

Reinforcement Learning (RL) has become a powerful tool for tackling complex sequential decision-making problems. It has been shown to train agents to reach super-human capabilities in game-playing domains such as Go and Atari. RL can also learn advanced control policies in high-dimensional robotic systems. Nevertheless, current RL agents have considerable difficulties when facing sparse rewards, long planning horizons, and more generally a scarcity of useful supervision signals. Unfortunately, the most valuable control tasks are specified in terms of high-level instructions, implying sparse rewards when formulated as an RL problem. Internal spatio-temporal abstractions and memory structures can constrain the decision space, improving data efficiency in the face of scarcity, but are likewise challenging for a supervisor to teach.

Hierarchical Reinforcement Learning (HRL) is emerging as a key component for finding spatio-temporal abstractions and behavioral patterns that can guide the discovery of useful large-scale control architectures, both for deep-network representations and for analytic and optimal-control methods. HRL has the potential to accelerate planning and exploration by identifying skills that can reliably reach desirable future states. It can abstract away the details of low-level controllers to facilitate long-horizon planning and meta-learning in a high-level feature space. Hierarchical structures are modular and amenable to separation of training efforts, reuse, and transfer. By imitating a core principle of human cognition, hierarchies hold promise for interpretability and explainability.

There is a growing interest in HRL methods for structure discovery, planning, and learning, as well as HRL systems for shared learning and policy deployment. The goal of this workshop is to improve cohesion and synergy among the research community and increase its impact by promoting better understanding of the challenges and potential of HRL. This workshop further aims to bring together researchers studying both theoretical and practical aspects of HRL, for a joint presentation, discussion, and evaluation of some of the numerous novel approaches to HRL developed in recent years.

While deep learning has been revolutionary for machine learning, most modern deep learning models cannot represent their uncertainty nor take advantage of the well studied tools of probability theory. This has started to change following recent developments of tools and techniques combining Bayesian approaches with deep learning. The intersection of the two fields has received great interest from the community over the past few years, with the introduction of new deep learning models that take advantage of Bayesian techniques, as well as Bayesian models that incorporate deep learning elements [1-11]. In fact, the use of Bayesian techniques in deep learning can be traced back to the 1990s’, in seminal works by Radford Neal [12], David MacKay [13], and Dayan et al. [14]. These gave us tools to reason about deep models’ confidence, and achieved state-of-the-art performance on many tasks. However earlier tools did not adapt when new needs arose (such as scalability to big data), and were consequently forgotten. Such ideas are now being revisited in light of new advances in the field, yielding many exciting new results.

Extending on last year’s workshop’s success, this workshop will again study the advantages and disadvantages of such ideas, and will be a platform to host the recent flourish of ideas using Bayesian approaches in deep learning and using deep learning tools in Bayesian modelling. The program includes a mix of invited talks, contributed talks, and contributed posters. It will be composed of five main themes: deep generative models, variational inference using neural network recognition models, practical approximate inference techniques in Bayesian neural networks, applications of Bayesian neural networks, and information theory in deep learning. Future directions for the field will be debated in a panel discussion.

Topics:
Probabilistic deep models for classification and regression (such as extensions and application of Bayesian neural networks),
Generative deep models (such as variational autoencoders),
Incorporating explicit prior knowledge in deep learning (such as posterior regularization with logic rules),
Approximate inference for Bayesian deep learning (such as variational Bayes / expectation propagation / etc. in Bayesian neural networks),
Scalable MCMC inference in Bayesian deep models,
Deep recognition models for variational inference (amortized inference),
Model uncertainty in deep learning,
Bayesian deep reinforcement learning,
Deep learning with small data,
Deep learning in Bayesian modelling,
Probabilistic semi-supervised learning techniques,
Active learning and Bayesian optimization for experimental design,
Applying non-parametric methods, one-shot learning, and Bayesian deep learning in general,
Implicit inference,
Kernel methods in Bayesian deep learning.


References:
[1] - Kingma, DP and Welling, M, ‘’Auto-encoding variational bayes’’, 2013.
[2] - Rezende, D, Mohamed, S, and Wierstra, D, ‘’Stochastic backpropagation and approximate inference in deep generative models’’, 2014.
[3] - Blundell, C, Cornebise, J, Kavukcuoglu, K, and Wierstra, D, ‘’Weight uncertainty in neural network’’, 2015.
[4] - Hernandez-Lobato, JM and Adams, R, ’’Probabilistic backpropagation for scalable learning of Bayesian neural networks’’, 2015.
[5] - Gal, Y and Ghahramani, Z, ‘’Dropout as a Bayesian approximation: Representing model uncertainty in deep learning’’, 2015.
[6] - Gal, Y and Ghahramani, G, ‘’Bayesian convolutional neural networks with Bernoulli approximate variational inference’’, 2015.
[7] - Kingma, D, Salimans, T, and Welling, M. ‘’Variational dropout and the local reparameterization trick’’, 2015.
[8] - Balan, AK, Rathod, V, Murphy, KP, and Welling, M, ‘’Bayesian dark knowledge’’, 2015.
[9] - Louizos, C and Welling, M, “Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors”, 2016.
[10] - Lawrence, ND and Quinonero-Candela, J, “Local distance preservation in the GP-LVM through back constraints”, 2006.
[11] - Tran, D, Ranganath, R, and Blei, DM, “Variational Gaussian Process”, 2015.
[12] - Neal, R, ‘’Bayesian Learning for Neural Networks’’, 1996.
[13] - MacKay, D, ‘’A practical Bayesian framework for backpropagation networks‘’, 1992.
[14] - Dayan, P, Hinton, G, Neal, R, and Zemel, S, ‘’The Helmholtz machine’’, 1995.
[15] - Wilson, AG, Hu, Z, Salakhutdinov, R, and Xing, EP, “Deep Kernel Learning”, 2016.
[16] - Saatchi, Y and Wilson, AG, “Bayesian GAN”, 2017.
[17] - MacKay, D.J.C. “Bayesian Methods for Adaptive Models”, PhD thesis, 1992.

This two day workshop will bring together researchers from the various subdisciplines of Geometric Data Analysis, such as manifold learning, topological data analysis, shape analysis, will showcase recent progress in this field and will establish directions for future research. The focus will be on high dimensional and big data, and on mathematically founded methodology.

Specific aims

One aim of this workshop is to build connections between Topological Data Analysis on one side and Manifold Learning on the other. This is starting to happen, after years of more or less separate evolution of the two fields. The moment has been reached when the mathematical, statistical and algorithmic foundations of both areas are mature enough -- it is now time to lay the foundations for joint topological and differential geometric understanding of data, and this workshop will expliecitly focus on this process.

The second aim is to bring GDA closer to real applications. We see the challenge of real problems and real data as a motivator for researchers to explore new research questions, to reframe and expand the existing theory, and to step out of their own sub-area. In particular, for people in GDA to see TDA and ML as one.

The impact of GDA in practice also depends on having scalable implementations of the most current results in theory. This workshop will showcase the GDA tools which achieve this and initiate a collective discussion about the tools that need to be built.

We intend this workshop to be a forum for researchers in all areas of Geometric Data Analysis. Trough the tutorials, we are reaching out to the wider NIPS audience, to the many potential users of of Geometric Data Analysis, to make them aware of the state of the art in GDA, and of the tools available. Last but not least, we hope that the scientists invited will bring these methods back to their communities.

Recent years have seen rapid progress in meta-learning methods, which learn (and optimize) the performance of learning methods based on data, generate new learning methods from scratch, and learn to transfer knowledge across tasks and domains. Meta-learning can be seen as the logical conclusion of the arc that machine learning has undergone in the last decade, from learning classifiers, to learning representations, and finally to learning algorithms that themselves acquire representations and classifiers. The ability to improve one’s own learning capabilities through experience can also be viewed as a hallmark of intelligent beings, and there are strong connections with work on human learning in neuroscience.

Meta-learning methods are also of substantial practical interest, since they have, e.g., been shown to yield new state-of-the-art automated machine learning methods, novel deep learning architectures, and substantially improved one-shot learning systems.

Some of the fundamental questions that this workshop aims to address are:
- What are the fundamental differences in the learning “task” compared to traditional “non-meta” learners?
- Is there a practical limit to the number of meta-learning layers (e.g., would a meta-meta-meta-learning algorithm be of practical use)?
- How can we design more sample-efficient meta-learning methods?
- How can we exploit our domain knowledge to effectively guide the meta-learning process?
- What are the meta-learning processes in nature (e.g, in humans), and how can we take inspiration from them?
- Which ML approaches are best suited for meta-learning, in which circumstances, and why?
- What principles can we learn from meta-learning to help us design the next generation of learning systems?

The goal of this workshop is to bring together researchers from all the different communities and topics that fall under the umbrella of meta-learning. We expect that the presence of these different communities will result in a fruitful exchange of ideas and stimulate an open discussion about the current challenges in meta-learning, as well as possible solutions.

In terms of prospective participants, our main targets are machine learning researchers interested in the processes related to understanding and improving current meta-learning algorithms. Specific target communities within machine learning include, but are not limited to: meta-learning, optimization, deep learning, reinforcement learning, evolutionary computation, Bayesian optimization and AutoML. Our invited speakers also include researchers who study human learning, to provide a broad perspective to the attendees.