This workshop bridges the conversation among different areas such as temporal knowledge graph learning, graph anomaly detection, and graph representation learning. It aims to share understanding and techniques to facilitate the development of novel temporal graph learning methods. It also brings together researchers from both academia and industry and connects researchers from various fields aiming to span theories, methodologies, and applications.

Discover how to improve the adoption of RL in practice, by discussing key research problems, SOTA, and success stories / insights / lessons w.r.t. practical RL algorithms, practical issues, and applications with leading experts from both academia and industry @ NeurIPS 2022 RL4RealLife workshop.

Eye gaze has proven to be a cost-efficient way to collect large-scale physiological data that can reveal the underlying human attentional patterns in real-life workflows, and thus has long been explored as a signal to directly measure human-related cognition in various domains. Physiological data (including but not limited to eye gaze) offer new perception capabilities, which could be used in several ML domains, e.g., egocentric perception, embodied AI, NLP, etc. They can help infer human perception, intentions, beliefs, goals, and other cognition properties that are much needed for human-AI interactions and agent coordination. In addition, large collections of eye-tracking data have enabled data-driven modeling of human visual attention mechanisms, both for saliency or scanpath prediction, with twofold advantages: from the neuroscientific perspective to understand biological mechanisms better, and from the AI perspective to equip agents with the ability to mimic or predict human behavior and improve interpretability and interactions.

With the emergence of immersive technologies, now more than any time there is a need for experts of various backgrounds (e.g., machine learning, vision, and neuroscience communities) to share expertise and contribute to a deeper understanding of the intricacies of cost-efficient human supervision signals (e.g., eye-gaze) and their utilization towards by bridging human cognition and AI in machine learning research and development. The goal of this workshop is to bring together an active research community to collectively drive progress in defining and addressing core problems in gaze-assisted machine learning.

As machine learning models permeate every aspect of decision making systems in consequential areas such as healthcare and criminal justice, it has become critical for these models to satisfy trustworthiness desiderata such as fairness, interpretability, accountability, privacy and security. Initially studied in isolation, recent work has emerged at the intersection of these different fields of research, leading to interesting questions on how fairness can be achieved using a causal perspective and under privacy concerns.

Indeed, the field of causal fairness has seen a large expansion in recent years notably as a way to counteract the limitations of initial statistical definitions of fairness. While a causal framing provides flexibility in modelling and mitigating sources of bias using a causal model, proposed approaches rely heavily on assumptions about the data generating process, i.e., the faithfulness and ignorability assumptions. This leads to open discussions on (1) how to fully characterize causal definitions of fairness, (2) how, if possible, to improve the applicability of such definitions, and (3) what constitutes a suitable causal framing of bias from a sociotechnical perspective?

Additionally, while most existing work on causal fairness assumes observed sensitive attribute data, such information is likely to be unavailable due to, for example, data privacy laws or ethical considerations. This observation has motivated initial work on training and evaluating fair algorithms without access to sensitive information and studying the compatibility and trade-offs between fairness and privacy. However, such work has been limited, for the most part, to statistical definitions of fairness raising the question of whether these methods can be extended to causal definitions.

Given the interesting questions that emerge at the intersection of these different fields, this workshop aims to deeply investigate how these different topics relate, but also how they can augment each other to provide better or more suited definitions and mitigation strategies for algorithmic fairness.

The Machine Learning and the Physical Sciences workshop aims to provide an informal, inclusive and leading-edge venue for research and discussions at the interface of machine learning (ML) and the physical sciences. This interface spans (1) applications of ML in physical sciences (ML for physics), (2) developments in ML motivated by physical insights (physics for ML), and most recently (3) convergence of ML and physical sciences (physics with ML) which inspires questioning what scientific understanding means in the age of complex-AI powered science, and what roles machine and human scientists will play in developing scientific understanding in the future.

Deep learning has flourished in the last decade. Recent breakthroughs have shown stunning results, and yet, researchers still cannot fully explain why neural networks generalise so well or why some architectures or optimizers work better than others. There is a lack of understanding of existing deep learning systems, which led NeurIPS 2017 test of time award winners Rahimi & Recht to compare machine learning with alchemy and to call for the return of the 'rigour police'.

Despite excellent theoretical work in the field, deep neural networks are so complex that they might not be able to be fully comprehended with theory alone. Unfortunately, the experimental alternative - rigorous work that neither proves a theorem nor proposes a new method - is currently under-valued in the machine learning community.

To change this, this workshop aims to promote the method of empirical falsification.

We solicit contributions which explicitly formulate a hypothesis related to deep learning or its applications (based on first principles or prior work), and then empirically falsify it through experiments. We further encourage submissions to go a layer deeper and investigate the causes of an initial idea not working as expected. This workshop will showcase how negative results offer important learning opportunities for deep learning researchers, possibly far greater than the incremental improvements found in conventional machine learning papers!

Why empirical falsification? In the words of Karl Popper, "It is easy to obtain confirmations, or verifications, for nearly every theory—if we look for confirmations. Confirmations should count only if they are the result of risky predictions."
We believe that similarly to physics, which seeks to understand nature, the complexity of deep neural networks makes any understanding about them built inductively likely to be brittle.

The most reliable method with which physicists can probe nature is by experimentally validating (or not) the falsifiable predictions made by their existing theories. We posit the same could be the case for deep learning and believe that the task of understanding deep neural networks would benefit from adopting the approach of empirical falsification.

Humanitarian crises from disease outbreak to war to oppression against disadvantaged groups have threatened people and their communities throughout history. Natural disasters are a single, extreme example of such crises. In the wake of hurricanes, earthquakes, and other such crises, people have ceaselessly sought ways--often harnessing innovation--to provide assistance to victims after disasters have struck.

Through this workshop, we intend to establish meaningful dialogue between the Artificial Intelligence (AI) and Humanitarian Assistance and Disaster Response (HADR) communities. By the end of the workshop, the NeurIPS research community can learn the practical challenges of aiding those in crisis, while the HADR community can get to know the state of art and practice in AI. We seek to establish a pipeline of transitioning the research created by the NeurIPS community to real-world humanitarian issues. We believe such an endeavor is possible due to recent successes in applying techniques from various AI and Machine Learning (ML) disciplines to HADR.

In recent years, there has been a growing appreciation for the importance of modeling the geometric structure in data — a perspective that has developed in both the geometric deep learning and applied geometry communities. In parallel, an emerging set of findings in neuroscience suggests that group-equivariance and the preservation of geometry and topology may be fundamental principles of neural coding in biology.

This workshop will bring together researchers from geometric deep learning and geometric statistics with theoretical and empirical neuroscientists whose work reveals the elegant implementation of geometric structure in biological neural circuitry. Group theory and geometry were instrumental in unifying models of fundamental forces and elementary particles in 20th-century physics. Likewise, they have the potential to unify our understanding of how neural systems form useful representations of the world.

The goal of this workshop is to unify the emerging paradigm shifts towards structured representations in deep networks and the geometric modeling of neural data — while promoting a solid mathematical foundation in algebra, geometry, and topology.

Welcome to the NeurIPS 2022 Workshop on Machine Learning for Autonomous Driving!

Autonomous vehicles (AVs) offer a rich source of high-impact research problems for the machine learning (ML) community; including perception, state estimation, probabilistic modeling, time series forecasting, gesture recognition, robustness guarantees, real-time constraints, user-machine communication, multi-agent planning, and intelligent infrastructure. Further, the interaction between ML subfields towards a common goal of autonomous driving can catalyze interesting inter-field discussions that spark new avenues of research, which this workshop aims to promote. As an application of ML, autonomous driving has the potential to greatly improve society by reducing road accidents, giving independence to those unable to drive, and even inspiring younger generations with tangible examples of ML-based technology clearly visible on local streets. All are welcome to attend! This will be the 7th NeurIPS workshop in this series. Previous workshops in 2016, 2017, 2018, 2019, 2020, and 2021 enjoyed wide participation from both academia and industry.

Machine Learning (ML) for Systems is an important direction for applying ML in the real world. It has been shown that ML can replace long standing heuristics in computer systems by leveraging supervised learning and reinforcement learning (RL) approaches. The computer systems community recognizes the importance of ML in tackling strenuous multi-objective tasks such as designing new data structures 1, integrated circuits 2,3, or schedulers, as well as implementing control algorithms for applications such as compilers 12,13, databases 8, memory management 9,10 or ML frameworks 6.

General Workshop Direction. This is the fifth iteration of this workshop. In previous editions, we showcased approaches and frameworks to solve problems, bringing together researchers and practitioners at NeurIPS from both ML and systems communities. While breaking new grounds, we encouraged collaborations and development in a broad range of ML for Systems works, many later published in top-tier conferences 6,13,14,15,16,17,18. This year, we plan to continue on this path while expanding our call for paper to encourage emerging works on minimizing energy footprint, reaching carbon neutrality, and using machine learning for system security and privacy.

Focusing the Workshop on Unifying Works. As the field of ML for Systems is maturing, we are adapting the focus and format of the workshop to evolve with it. The community has seen several efforts to consolidate different subfields of ML for Systems 4, 5, 6, 7. However, such efforts need more support. To boost recent advances in shared methodology, tools, and frameworks, this year we will welcome submissions presenting datasets, simulators, or benchmarks that can facilitate research in the area.

In only a few years, structural biology, the study of the 3D structure or shape of proteins and other biomolecules, has been transformed by breakthroughs from machine learning algorithms. Machine learning models are now routinely being used by experimentalists to predict structures that can help answer real biological questions (e.g. AlphaFold), accelerate the experimental process of structure determination (e.g. computer vision algorithms for cryo-electron microscopy), and have become a new industry standard for bioengineering new protein therapeutics (e.g. large language models for protein design). Despite all this progress, there are still many active and open challenges for the field, such as modeling protein dynamics, predicting higher order complexes, pushing towards generalization of protein folding physics, and relating the structure of proteins to the in vivo and contextual nature of their underlying function. These challenges are diverse and interdisciplinary, motivating new kinds of machine learning systems and requiring the development and maturation of standard benchmarks and datasets.

In this exciting time for the field, our workshop, “Machine Learning in Structural Biology” (MLSB), seeks to bring together relevant experts, practitioners, and students across a broad community to focus on these challenges and opportunities. We believe the union of these communities, including the geometric and graph learning communities, NLP researchers, and structural biologists with domain expertise at our workshop can help spur new ideas, spark collaborations, and advance the impact of machine learning in structural biology. Progress at this intersection promises to unlock new scientific discoveries and the ability to design novel medicines.

Many cognitive and neural systems can be described in terms of compression and transmission of information given bounded resources. While information theory, as a principled mathematical framework for characterizing such systems, has been widely applied in neuroscience and machine learning, its role in understanding cognition has traditionally been contested. This traditional view has been changing in recent years, with growing evidence that information-theoretic optimality principles underlie a wide range of cognitive functions, including perception, working memory, language, and decision making. In parallel, there has also been a surge of contemporary information-theoretic approaches in machine learning, enabling large-scale neural-network implementation of information-theoretic models.

These scientific and technological developments open up new avenues for progress toward an integrative computational theory of human and artificial cognition, by leveraging information-theoretic principles as bridges between various cognitive functions and neural representations. This workshop aims to explore these new research directions and bring together researchers from machine learning, cognitive science, neuroscience, linguistics, economics, and potentially other fields, who are interested in integrating information-theoretic approaches that have thus far been studied largely independently of each other. In particular, we aim to discuss questions and exchange ideas along the following directions:

- Understanding human cognition: To what extent can information theoretic principles advance the understanding of human cognition and its emergence from neural systems? What are the key challenges for future research in information theory and cognition? How might tools from machine learning help overcome these challenges? Addressing such questions could lead to progress in computational models that integrate multiple cognitive functions and cross Marr’s levels of analysis.

- Improving AI agents and human-AI cooperation: Given empirical evidence that information theoretic principles may underlie a range of human cognitive functions, how can such principles guide artificial agents toward human-like cognition? How might these principles facilitate human-AI communication and cooperation? Can this help agents learn faster with less data? Addressing such questions could lead to progress in developing better human-like AI systems.

This workshop aims to discuss the challenges and opportunities of expanding research collaborations in light of the changing landscape of where, how, and by whom research is produced. Progress toward democratizing AI research has been centered around making knowledge (e.g. class materials), established ideas (e.g. papers), and technologies (e.g. code, compute) more accessible. However, open, online resources are only part of the equation. Growth as a researcher requires not only learning by consuming information individually, but hands-on practice whiteboarding, coding, plotting, debugging, and writing collaboratively, with either mentors or peers. Of course, making "collaborators" more universally accessible is fundamentally more difficult than, say, ensuring all can access arXiv papers because scaling people and research groups is much harder than scaling websites. Can we nevertheless make access to collaboration itself more open?

Recent rapid development of machine learning has largely benefited from algorithmic advances, collection of large-scale datasets, and availability of high-performance computation resources, among others. However, the large volume of collected data and massive information may also bring serious security, privacy, services provisioning, and network management challenges. In order to achieve decentralized, secure, private, and trustworthy machine learning operation and data management in this “data-centric AI” era, the joint consideration of blockchain techniques and machine learning may bring significant benefits and have attracted great interest from both academia and industry. On the one hand, decentralization and blockchain techniques can significantly facilitate training data and machine learning model sharing, decentralized intelligence, security, privacy, and trusted decision-making. On the other hand, Web3 platforms and applications, which are built on blockchain technologies and token-based economics, will greatly benefit from machine learning techniques in resource efficiency, scalability, trustworthy machine learning, and other ML-augmented tools for creators and participants in the end-to-end ecosystems.

This workshop focuses on how future researchers and practitioners should prepare themselves to achieve different trustworthiness requirements, such as security and privacy in machine learning through decentralization and blockchain techniques, as well as how to leverage machine learning techniques to automate some processes in current decentralized systems and ownership economies in Web3. We attempt to share recent related work from different communities, discuss the foundations of trustworthiness problems in machine learning and potential solutions, tools, and platforms via decentralization, blockchain and Web3, and chart out important directions for future work and cross-community collaborations.

Transfer learning from large pre-trained language models (PLM) has become the de-facto method for a wide range of natural language processing tasks. Current transfer learning methods, combined with PLMs, have seen outstanding successes in transferring knowledge to new tasks, domains, and even languages. However, existing methods, including fine-tuning, in-context learning, parameter-efficient tuning, semi-parametric models with knowledge augmentation, etc., still lack consistently good performance across different tasks, domains, varying sizes of data resources, and diverse textual inputs.

This workshop aims to invite researchers from different backgrounds to share their latest work in efficient and robust transfer learning methods, discuss challenges and risks of transfer learning models when deployed in the wild, understand positive and negative transfer, and also debate over future directions.

Humans acquire vision, language, and decision making abilities through years of experience, arguably corresponding to millions of video frames, audio clips, and interactions with the world. Following this data-driven approach, recent foundation models trained on large and diverse datasets have demonstrated emergent capabilities and fast adaptation to a wide range of downstream vision and language tasks (e.g., BERT, DALL-E, GPT-3, CLIP). Meanwhile in the decision making and reinforcement learning (RL) literature, foundation models have yet to fundamentally shift the traditional paradigm in which an agent learns from its own or others’ collected experience, typically on a single-task and with limited prior knowledge. Nevertheless, there has been a growing body of foundation-model-inspired research in decision making that often involves collecting large amounts of interactive data for self-supervised learning at scale. For instance, foundation models such as BERT and GPT-3 have been applied to modeling trajectory sequences of agent experience, and ever-larger datasets have been curated for learning multimodel, multitask, and generalist agents. These works demonstrate the potential benefits of foundation models on a broad set of decision making applications such as autonomous driving, healthcare systems, robotics, goal-oriented dialogue, robotics, and recommendation systems.

Despite early signs of success, foundation models for decision making remain largely underexplored, underutilized, and lacking solid empirical and theoretical grounding. The challenges faced by existing research are as follows:
1. Many traditional decision making benchmarks are (near-)Markovian (i.e., historyless), and this brings the value of sequence modeling into question. The true power of foundation models may require more complex tasks.
2. Decision making tasks are composed of multi-modal data. At minimum, the states (observations), actions, and rewards of a task are each of different types. Moreover, across different tasks, states and actions can be highly distinct (image vs. text observations, discrete vs. continuous actions).
3. Unlike vision and language, decision making agents can further interact with the environment to collect additional experience in conjunction with learning on existing data. How such an interactive component should be integrated with foundation models is not clear.
4. There already exhibits a large gap between theory and practice in decision making. Hastily applying large models to decision making might create an even greater gap.

Goal of the workshop: The goal of this workshop is to bring together the decision making community and the foundation models community in vision and language to confront the challenges in decision making at scale. The workshop will span high-level discussions on how foundation models can help decision making (if at all) and low-level algorithmic differences of decision, vision, and language which might lead to both opportunities or challenges for applying foundation models to decision making. More specific topics will include but are not limited to:
1. Common or distinct properties of vision, language, and decision making tasks that reassure or challenge the value of foundation models in decision making.
2. Introduction or proposals for new benchmarks to facilitate better research for foundation models for decision making.
3. How decision making can benefit from techniques already popular for foundation models, such as autoregressive sequence models, diffusion models, contrastive pretraining, masked autoencoders, prompting, etc.
4. Lessons learned from developing engineering frameworks, datasets and benchmarks, and evaluation protocols for foundation models in vision and language, and how can the decision making community benefit from these lessons.
5. How foundation models relate to the theoretical foundations of sequential decision making.

OPT 2022 will bring experts in optimization to share their perspectives while leveraging crossover experts in ML to share their views and recent advances. OPT 2022 honors this tradition of bringing together people from optimization and from ML in order to promote and generate new interactions between the two communities.

To foster the spirit of innovation and collaboration, a goal of this workshop, OPT 2022 will focus the contributed talks on research in Reliable Optimization Methods for ML. Many optimization algorithms for ML were originally developed with the goal of handling computational constraints (e.g., stochastic gradient based algorithms). Moreover, the analyses of these algorithms followed the classical optimization approach where one measures the performances of algorithms based on (i) the computation cost and (ii) convergence for any input into the algorithm. As engineering capabilities increase and the wide adoption of ML into many real world usages, practitioners of ML are seeking optimization algorithms that go beyond finding the minimizer with the fastest algorithm. They want reliable methods that solve real-world complications that arise. For example, increasingly bad actors are attempting to fool models with deceptive data. This leads to questions such as what algorithms are more robust to adversarial attacks and can one design new algorithms that can thwart these attacks? The latter question motivates a new area of optimization focusing on game-theoretic environments, that is, environments where there are competing forces at play and devising guarantees. Beyond this, a main reason for the success of ML is that optimization algorithms seemingly generate points that learn from training data; that is, we want minimizers of training data to provide meaningful interpretations on new data (generalization) yet we do not understand what features (e.g., loss function, algorithm, depth of the architectures (deep learning), and/or training samples) yield better generalization properties. These new areas of solving practical ML problems and their deep ties to the optimization community warrants a necessary discussion between the two communities. Specifically, we aim to discuss the meanings of generalization as well as the challenges facing real-world applications of ML and the new paradigms for optimizers seeking to solve them.

Plenary Speakers: All invited speakers have agreed to coming in-person to the workshop.

* Niao He (ETH, Zurich, assistant professor)

* Zico Kolter (Carnegie Mellon University, associate professor)

* Lorenzo Rosasco (U Genova/MIT, assistant professor)

* Katya Scheinberg (Cornell, full professor)

* Aaron Sidford (Stanford, assistant professor)

Mathematical reasoning is a unique aspect of human intelligence and a fundamental building block for scientific and intellectual pursuits. However, learning mathematics is often a challenging human endeavor that relies on expert instructors to create, teach and evaluate mathematical material. From an educational perspective, AI systems that aid in this process offer increased inclusion and accessibility, efficiency, and understanding of mathematics. Moreover, building systems capable of understanding, creating, and using mathematics offers a unique setting for studying reasoning in AI. This workshop will investigate the intersection of mathematics education and AI.

Interactive machine learning studies algorithms that learn from data collected through interaction with either a computational or human agent in a shared environment, through feedback on model decisions. In contrast to the common paradigm of supervised learning, IML does not assume access to pre-collected labeled data, thereby decreasing data costs. Instead, it allows systems to improve over time, empowering non-expert users to provide feedback. IML has seen wide success in areas such as video games and recommendation systems.
Although most downstream applications of NLP involve interactions with humans - e.g., via labels, demonstrations, corrections, or evaluation - common NLP models are not built to learn from or adapt to users through interaction. There remains a large research gap that must be closed to enable NLP systems that adapt on-the-fly to the changing needs of humans and dynamic environments through interaction.

This workshop brings together domain experts and ML researchers working on mitigating distribution shifts in real-world applications.

Distribution shifts—where a model is deployed on a data distribution different from what it was trained on—pose significant robustness challenges in real-world ML applications. Such shifts are often unavoidable in the wild and have been shown to substantially degrade model performance in applications such as biomedicine, wildlife conservation, sustainable development, robotics, education, and criminal justice. For example, models can systematically fail when tested on patients from different hospitals or people from different demographics.

This workshop aims to convene a diverse set of domain experts and methods-oriented researchers working on distribution shifts. We are broadly interested in methods, evaluations and benchmarks, and theory for distribution shifts, and we are especially interested in work on distribution shifts that arise naturally in real-world application contexts. Examples of relevant topics include, but are not limited to:
- Examples of real-world distribution shifts in various application areas. We especially welcome applications that are not widely discussed in the ML research community, e.g., education, sustainable development, and conservation. We encourage submissions that characterize distribution shifts and their effects in real-world applications; it is not at all necessary to propose a solution that is algorithmically novel.
- Methods for improving robustness to distribution shifts. Relevant settings include domain generalization, domain adaptation, and subpopulation shifts, and we are interested in a wide range of approaches, from uncertainty estimation to causal inference to active data collection. We welcome methods that can work across a variety of shifts, as well as more domain-specific methods that incorporate prior knowledge on the types of shifts we wish to be robust on. We encourage evaluating these methods on real-world distribution shifts.
- Empirical and theoretical characterization of distribution shifts. Distribution shifts can vary widely in the way in which the data distribution changes, as well as the empirical trends they exhibit. What empirical trends do we observe? What empirical or theoretical frameworks can we use to characterize these different types of shifts and their effects? What kinds of theoretical settings capture useful components of real-world distribution shifts?
- Benchmarks and evaluations. We especially welcome contributions for subpopulation shifts, as they are underrepresented in current ML benchmarks. We are also interested in evaluation protocols that move beyond the standard assumption of fixed training and test splits -- for which applications would we need to consider other forms of shifts, such as streams of continually-changing data or feedback loops between models and data?