Timezone: »
Driven by cheap commodity storage, fast data networks, rich structured models, and the increasing desire to catalog and share our collective experiences in real-time, the scale of many important learning problems has grown well beyond the capacity of traditional sequential systems. These “Big Learning” problems arise in many domains including bioinformatics, astronomy, recommendation systems, social networks, computer vision, web search and online advertising. Simultaneously, parallelism has emerged as a dominant widely used computational paradigm in devices ranging from energy efficient mobile processors, to desktop supercomputers in the form of GPUs, to massively scalable cloud computing services. The Big Learning setting has attracted intense interest across industry and academia, with active research spanning diverse fields ranging from machine learning and databases to large scale distributed systems and programming languages. However because the Big Learning setting is being studied by experts of these various communities, there is a need for a common venue to discuss recent progress, to identify pressing new challenges, and to exchange new ideas.
This workshop aims to:
* Bring together parallel and distributed system builders in industry and academia, machine learning experts, and end users to identify the key challenges, opportunities, and myths of Big Learning. What REALLY changes from the traditional learning setting when faced with terabytes or petabytes of data?
* Solicit practical case studies, demos, benchmarks and lessons-learned presentations, and position papers.
* Showcase recent and ongoing progress towards parallel ML algorithms
* Provide a forum for exchange regarding tools, software, and systems that address the Big Learning problem.
* Educate the researchers and practitioners across communities on state-of-the-art solutions and their limitations, particularly focusing on key criteria for selecting task- and domain-appropriate platforms and algorithms.
Focal points for discussions and solicited submissions include but are not limited to:
1. Case studies of practical applications that operate on large data sets or computationally intensive models; typical data and workflow patterns; machine learning challenges and lessons learned.
2. Insights about the end users for large-scale learning: who are they, what are their needs, what expertise is required of them?
3. Common data characteristics: is it more typical for data to appear in streams or in batches? What are the applications that demand online or real-time learning, and how can the engineering challenges for deploying autonomously adaptive systems be overcome? Which analytic and learning problems are more appropriate for (or even require) analysis in the cloud, and when is “desktop” learning on sub-sampled or compressed data sufficient?
4. Choices in data storage and management, e.g., trade-offs between classical RDBMS and NoSQL platforms from a data analysis and machine learning perspectives.
5. The feasibility of alternate structured data storage: object databases, graph databases, and streams.
6. Suitability of different distributed system platforms and programming paradigms: Hadoop, DryadLINQ, EC2, Azure, etc.
7. Applicability of different learning and analysis techniques: prediction models that require large-scale training, vs. simpler data analysis (e.g., summary statistics), which is needed when.
8. Computationally intensive learning and inference: Big Learning doesn’t just mean 9. Big Data it also can mean massive models or structured prediction tasks.
Labeling and supervision: scenarios for large-scale label availability and appropriate learning approaches. Making use of diverse labeling strategies (curated vs. noisy/crowd-sourced/feedback-based labeling)
10. Real-world deployment issues: initial prototyping requires quickly-implemented-and-expandable solutions, along with the ability to easily incorporate new features/data sources.
11. Practicality of high-performance hardware for large-scale learning (e.g., GPUs, FPGAs, ASIC). GPU vs. CPU processors: programming strategies and performance opportunities and tradeoffs.
12. Unifying the disparate data structures and software libraries that have emerged in the GP-GPU community.
13. Evaluation methodology and trade-offs between machine learning metrics (predictive accuracy), computational performance (throughput, latency, speedup), and engineering complexity and cost.
14. Principled methods for dealing with huge numbers of features. As the number of data points grow, often times so do the number of features as well as their dependence structure. Does Big Learning require, for example, better ways of doing multiple hypothesis testing than FDR?
15. Determination of when is an answer good enough. How can we efficiently estimate confidence intervals over Big Data?
Target audience includes industry and academic researchers from the various subfields relevant to large-scale machine learning, with a strong bias for either position talks that aim to induce discussion, or accessible overviews of the state-of-the-art. We will solicit paper submissions in the form of short, long and position papers as well as demo proposals. Papers that focus on emerging applications or deployment case studies will be particularly encouraged, while demos of operational toolkits and platforms will be considered for inclusion in the primary program of the workshop.
Author Information
Joseph E Gonzalez (Carnegie Mellon University)
Sameer Singh (University of California, Irvine)
Graham Taylor (University of Guelph)
James Bergstra (Kindred)
Alice Zheng (GraphLab Inc)
Misha Bilenko (Yandex)
Yucheng Low (Apple)
Yoshua Bengio (University of Montreal)
Yoshua Bengio (PhD'1991 in Computer Science, McGill University). After two post-doctoral years, one at MIT with Michael Jordan and one at AT&T Bell Laboratories with Yann LeCun, he became professor at the department of computer science and operations research at Université de Montréal. Author of two books (a third is in preparation) and more than 200 publications, he is among the most cited Canadian computer scientists and is or has been associate editor of the top journals in machine learning and neural networks. Since '2000 he holds a Canada Research Chair in Statistical Learning Algorithms, since '2006 an NSERC Chair, since '2005 his is a Senior Fellow of the Canadian Institute for Advanced Research and since 2014 he co-directs its program focused on deep learning. He is on the board of the NIPS foundation and has been program chair and general chair for NIPS. He has co-organized the Learning Workshop for 14 years and co-created the International Conference on Learning Representations. His interests are centered around a quest for AI through machine learning, and include fundamental questions on deep learning, representation learning, the geometry of generalization in high-dimensional spaces, manifold learning and biologically inspired learning algorithms.
Michael Franklin (UC Berkeley)
Carlos Guestrin (University of Washington)
Andrew McCallum (UMass Amherst)
Alexander Smola (Amazon - We are hiring!)
**AWS Machine Learning**
Michael Jordan (UC Berkeley)
Sugato Basu (Google Research)
More from the Same Authors
-
2020 : Building LEGO using Deep Generative Models of Graphs »
Rylee Thompson · Graham Taylor · Terrance DeVries · Elahe Ghalebi -
2021 Spotlight: Learning Equilibria in Matching Markets from Bandit Feedback »
Meena Jagadeesan · Alexander Wei · Yixin Wang · Michael Jordan · Jacob Steinhardt -
2021 Spotlight: Mixture Proportion Estimation and PU Learning:A Modern Approach »
Saurabh Garg · Yifan Wu · Alexander Smola · Sivaraman Balakrishnan · Zachary Lipton -
2021 Spotlight: Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization »
Kartik Ahuja · Ethan Caballero · Dinghuai Zhang · Jean-Christophe Gagnon-Audet · Yoshua Bengio · Ioannis Mitliagkas · Irina Rish -
2021 Spotlight: Robust Learning of Optimal Auctions »
Wenshuo Guo · Michael Jordan · Emmanouil Zampetakis -
2021 : Benchmarking Multimodal AutoML for Tabular Data with Text Fields »
Xingjian Shi · Jonas Mueller · Nick Erickson · Mu Li · Alexander Smola -
2021 : Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning »
Nan Rosemary Ke · Aniket Didolkar · Sarthak Mittal · Anirudh Goyal · Guillaume Lajoie · Stefan Bauer · Danilo Jimenez Rezende · Yoshua Bengio · Chris Pal · Michael Mozer -
2021 : CSFCube - A Test Collection of Computer Science Research Articles for Faceted Query by Example »
Sheshera Mysore · Tim O'Gorman · Andrew McCallum · Hamed Zamani -
2021 : Optimization with Adaptive Step Size Selection from a Dynamical Systems Perspective »
Neha Wadia · Michael Jordan · Michael Muehlebach -
2021 : Optimization with Adaptive Step Size Selection from a Dynamical Systems Perspective »
Neha Wadia · Michael Jordan · Michael Muehlebach -
2021 : Last-Iterate Convergence of Saddle Point Optimizers via High-Resolution Differential Equations »
Tatjana Chavdarova · Michael Jordan · Emmanouil Zampetakis -
2021 : On the convergence of stochastic extragradient for bilinear games using restarted iteration averaging »
Chris Junchi Li · Yaodong Yu · Nicolas Loizou · Gauthier Gidel · Yi Ma · Nicolas Le Roux perso · Michael Jordan -
2021 : On the convergence of stochastic extragradient for bilinear games using restarted iteration averaging »
Chris Junchi Li · Yaodong Yu · Nicolas Loizou · Gauthier Gidel · Yi Ma · Nicolas Le Roux perso · Michael Jordan -
2021 : Cutting Down on Prompts and Parameters:Simple Few-Shot Learning with Language Models »
Robert Logan · Ivana Balazevic · Eric Wallace · Fabio Petroni · Sameer Singh · Sebastian Riedel -
2021 : Long-Term Credit Assignment via Model-based Temporal Shortcuts »
Michel Ma · Pierluca D'Oro · Yoshua Bengio · Pierre-Luc Bacon -
2021 : GPU-Podracer: Scalable and Elastic Library for Cloud-Native Deep Reinforcement Learning »
Xiao-Yang Liu · Zhuoran Yang · Zhaoran Wang · Anwar Walid · Jian Guo · Michael Jordan -
2021 : Learning Two-Player Mixture Markov Games: Kernel Function Approximation and Correlated Equilibrium »
Chris Junchi Li · Dongruo Zhou · Quanquan Gu · Michael Jordan -
2021 : A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning »
Mingde Zhao · Zhen Liu · Sitao Luan · Shuyuan Zhang · Doina Precup · Yoshua Bengio -
2021 : Effect of diversity in Meta-Learning »
Ramnath Kumar · Tristan Deleu · Yoshua Bengio -
2021 : Desiderata for Representation Learning: A Causal Perspective »
Yixin Wang · Michael Jordan -
2021 : An Empirical Study of Neural Kernel Bandits »
Michal Lisicki · Arash Afkanpour · Graham Taylor -
2022 Poster: Rank Diminishing in Deep Neural Networks »
Ruili Feng · Kecheng Zheng · Yukun Huang · Deli Zhao · Michael Jordan · Zheng-Jun Zha -
2022 : Nesterov Meets Optimism: Rate-Optimal Optimistic-Gradient-Based Method for Stochastic Bilinearly-Coupled Minimax Optimization »
Chris Junchi Li · Angela Yuan · Gauthier Gidel · Michael Jordan -
2022 : Solving Constrained Variational Inequalities via a First-order Interior Point-based Method »
Tong Yang · Michael Jordan · Tatjana Chavdarova -
2022 : Perseus: A Simple and Optimal High-Order Method for Variational Inequalities »
Tianyi Lin · Michael Jordan -
2022 : Towards Provably Personalized Federated Learning via Threshold-Clustering of Similar Clients »
Mariel A Werner · Lie He · Sai Praneeth Karimireddy · Michael Jordan · Martin Jaggi -
2022 : RLSBench: A Large-Scale Empirical Study of Domain Adaptation Under Relaxed Label Shift »
Saurabh Garg · Nick Erickson · James Sharpnack · Alexander Smola · Sivaraman Balakrishnan · Zachary Lipton -
2022 : Quantifying Social Biases Using Templates is Unreliable »
Preethi Seshadri · Pouya Pezeshkpour · Sameer Singh -
2022 : TalkToModel: Explaining Machine Learning Models with Interactive Natural Language Conversations »
Dylan Slack · Satyapriya Krishna · Himabindu Lakkaraju · Sameer Singh -
2022 : Valid Inference after Causal Discovery »
Paula Gradu · Tijana Zrnic · Yixin Wang · Michael Jordan -
2022 : A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning »
Zixiang Chen · Chris Junchi Li · Angela Yuan · Quanquan Gu · Michael Jordan -
2022 : Contributed Talk: TalkToModel: Explaining Machine Learning Models with Interactive Natural Language Conversations »
Dylan Slack · Satyapriya Krishna · Himabindu Lakkaraju · Sameer Singh -
2022 : Mechanisms that Incentivize Data Sharing in Federated Learning »
Sai Praneeth Karimireddy · Wenshuo Guo · Michael Jordan -
2022 Poster: Adaptive Interest for Emphatic Reinforcement Learning »
Martin Klissarov · Rasool Fakoor · Jonas Mueller · Kavosh Asadi · Taesup Kim · Alexander Smola -
2022 Poster: Off-Policy Evaluation with Policy-Dependent Optimization Response »
Wenshuo Guo · Michael Jordan · Angela Zhou -
2022 Poster: First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces »
Michael Jordan · Tianyi Lin · Emmanouil-Vasileios Vlatakis-Gkaragkounis -
2022 Poster: Modeling Transitivity and Cyclicity in Directed Graphs via Binary Code Box Embeddings »
Dongxu Zhang · Michael Boratko · Cameron Musco · Andrew McCallum -
2022 Poster: Faster Deep Reinforcement Learning with Slower Online Network »
Kavosh Asadi · Rasool Fakoor · Omer Gottesman · Taesup Kim · Michael Littman · Alexander Smola -
2022 Poster: Learning Two-Player Markov Games: Neural Function Approximation and Correlated Equilibrium »
Chris Junchi Li · Dongruo Zhou · Quanquan Gu · Michael Jordan -
2022 Poster: Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets »
Yifei Min · Tianhao Wang · Ruitu Xu · Zhaoran Wang · Michael Jordan · Zhuoran Yang -
2022 Poster: Robust Calibration with Multi-domain Temperature Scaling »
Yaodong Yu · Stephen Bates · Yi Ma · Michael Jordan -
2022 Poster: On-Demand Sampling: Learning Optimally from Multiple Distributions »
Nika Haghtalab · Michael Jordan · Eric Zhao -
2022 Poster: Graph Reordering for Cache-Efficient Near Neighbor Search »
Benjamin Coleman · Santiago Segarra · Alexander Smola · Anshumali Shrivastava -
2022 Poster: Gradient-Free Methods for Deterministic and Stochastic Nonsmooth Nonconvex Optimization »
Tianyi Lin · Zeyu Zheng · Michael Jordan -
2022 Poster: TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels »
Yaodong Yu · Alexander Wei · Sai Praneeth Karimireddy · Yi Ma · Michael Jordan -
2022 Poster: Empirical Gateaux Derivatives for Causal Inference »
Michael Jordan · Yixin Wang · Angela Zhou -
2022 Poster: Structured Energy Network As a Loss »
Jay Yoon Lee · Dhruvesh Patel · Purujit Goyal · Wenlong Zhao · Zhiyang Xu · Andrew McCallum -
2021 : Panel Discussion »
Pascal Poupart · Ali Ghodsi · Luke Zettlemoyer · Sameer Singh · Kevin Duh · Yejin Choi · Lu Hou -
2021 : DeepRNG: Towards Deep Reinforcement Learning-Assisted Generative Testing of Software »
Chuan-Yung Tsai · Graham Taylor -
2021 : How to Win LMs and Influence Predictions: Using Short Phrases to Control NLP Models »
Sameer Singh -
2021 : Cutting Down on Prompts and Parameters:Simple Few-Shot Learning with Language Models »
Robert Logan · Ivana Balazevic · Eric Wallace · Fabio Petroni · Sameer Singh · Sebastian Riedel -
2021 : Neural Structure Mapping For Learning Abstract Visual Analogies »
Shashank Shekhar · Graham Taylor -
2021 Poster: Capacity and Bias of Learned Geometric Embeddings for Directed Graphs »
Michael Boratko · Dongxu Zhang · Nicholas Monath · Luke Vilnis · Kenneth L Clarkson · Andrew McCallum -
2021 Poster: Dynamic Inference with Neural Interpreters »
Nasim Rahaman · Muhammad Waleed Gondal · Shruti Joshi · Peter Gehler · Yoshua Bengio · Francesco Locatello · Bernhard Schölkopf -
2021 Poster: Robust Learning of Optimal Auctions »
Wenshuo Guo · Michael Jordan · Emmanouil Zampetakis -
2021 Poster: Reliable Post hoc Explanations: Modeling Uncertainty in Explainability »
Dylan Slack · Anna Hilgard · Sameer Singh · Himabindu Lakkaraju -
2021 Poster: Learning in Multi-Stage Decentralized Matching Markets »
Xiaowu Dai · Michael Jordan -
2021 : PYLON: A PyTorch Framework for Learning with Constraints »
Kareem Ahmed · Tao Li · Nu Mai Thy Ton · Quan Guo · Kai-Wei Chang · Parisa Kordjamshidi · Vivek Srikumar · Guy Van den Broeck · Sameer Singh -
2021 Poster: Gradient Starvation: A Learning Proclivity in Neural Networks »
Mohammad Pezeshki · Oumar Kaba · Yoshua Bengio · Aaron Courville · Doina Precup · Guillaume Lajoie -
2021 Poster: Mixture Proportion Estimation and PU Learning:A Modern Approach »
Saurabh Garg · Yifan Wu · Alexander Smola · Sivaraman Balakrishnan · Zachary Lipton -
2021 Poster: Who Leads and Who Follows in Strategic Classification? »
Tijana Zrnic · Eric Mazumdar · Shankar Sastry · Michael Jordan -
2021 Poster: Deep Explicit Duration Switching Models for Time Series »
Abdul Fatir Ansari · Konstantinos Benidis · Richard Kurle · Ali Caner Turkmen · Harold Soh · Alexander Smola · Bernie Wang · Tim Januschowski -
2021 Poster: Test-time Collective Prediction »
Celestine Mendler-Dünner · Wenshuo Guo · Stephen Bates · Michael Jordan -
2021 Poster: On the Theory of Reinforcement Learning with Once-per-Episode Feedback »
Niladri Chatterji · Aldo Pacchiano · Peter Bartlett · Michael Jordan -
2021 Poster: Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learning »
Hyunsoo Chung · Jungtaek Kim · Boris Knyazev · Jinhwi Lee · Graham Taylor · Jaesik Park · Minsu Cho -
2021 Poster: Wasserstein Flow Meets Replicator Dynamics: A Mean-Field Analysis of Representation Learning in Actor-Critic »
Yufeng Zhang · Siyu Chen · Zhuoran Yang · Michael Jordan · Zhaoran Wang -
2021 Poster: Tactical Optimism and Pessimism for Deep Reinforcement Learning »
Ted Moskovitz · Jack Parker-Holder · Aldo Pacchiano · Michael Arbel · Michael Jordan -
2021 Poster: Continuous Doubly Constrained Batch Reinforcement Learning »
Rasool Fakoor · Jonas Mueller · Kavosh Asadi · Pratik Chaudhari · Alexander Smola -
2021 Poster: A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning »
Mingde Zhao · Zhen Liu · Sitao Luan · Shuyuan Zhang · Doina Precup · Yoshua Bengio -
2021 Poster: Neural Production Systems »
Anirudh Goyal · Aniket Didolkar · Nan Rosemary Ke · Charles Blundell · Philippe Beaudoin · Nicolas Heess · Michael Mozer · Yoshua Bengio -
2021 Poster: Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation »
Emmanuel Bengio · Moksh Jain · Maksym Korablyov · Doina Precup · Yoshua Bengio -
2021 Poster: Parameter Prediction for Unseen Deep Architectures »
Boris Knyazev · Michal Drozdzal · Graham Taylor · Adriana Romero Soriano -
2021 Poster: Learning Equilibria in Matching Markets from Bandit Feedback »
Meena Jagadeesan · Alexander Wei · Yixin Wang · Michael Jordan · Jacob Steinhardt -
2021 Poster: The Causal-Neural Connection: Expressiveness, Learnability, and Inference »
Kevin Xia · Kai-Zhan Lee · Yoshua Bengio · Elias Bareinboim -
2021 Poster: Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization »
Kartik Ahuja · Ethan Caballero · Dinghuai Zhang · Jean-Christophe Gagnon-Audet · Yoshua Bengio · Ioannis Mitliagkas · Irina Rish -
2021 Poster: Discrete-Valued Neural Communication »
Dianbo Liu · Alex Lamb · Kenji Kawaguchi · Anirudh Goyal · Chen Sun · Michael Mozer · Yoshua Bengio -
2021 Poster: On Component Interactions in Two-Stage Recommender Systems »
Jiri Hron · Karl Krauth · Michael Jordan · Niki Kilbertus -
2021 Poster: Counterfactual Explanations Can Be Manipulated »
Dylan Slack · Anna Hilgard · Himabindu Lakkaraju · Sameer Singh -
2020 : Contributed Talk 6: Do Offline Metrics Predict Online Performance in Recommender Systems? »
Karl Krauth · Sarah Dean · Wenshuo Guo · Benjamin Recht · Michael Jordan -
2020 Poster: Projection Robust Wasserstein Distance and Riemannian Optimization »
Tianyi Lin · Chenyou Fan · Nhat Ho · Marco Cuturi · Michael Jordan -
2020 Poster: Fixed-Support Wasserstein Barycenters: Computational Hardness and Fast Algorithm »
Tianyi Lin · Nhat Ho · Xi Chen · Marco Cuturi · Michael Jordan -
2020 Spotlight: Projection Robust Wasserstein Distance and Riemannian Optimization »
Tianyi Lin · Chenyou Fan · Nhat Ho · Marco Cuturi · Michael Jordan -
2020 Poster: Decision-Making with Auto-Encoding Variational Bayes »
Romain Lopez · Pierre Boyeau · Nir Yosef · Michael Jordan · Jeffrey Regier -
2020 Poster: Transferable Calibration with Lower Bias and Variance in Domain Adaptation »
Ximei Wang · Mingsheng Long · Jianmin Wang · Michael Jordan -
2020 Poster: Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation »
Rasool Fakoor · Jonas Mueller · Nick Erickson · Pratik Chaudhari · Alexander Smola -
2020 Poster: Instance Selection for GANs »
Terrance DeVries · Michal Drozdzal · Graham Taylor -
2020 Tutorial: (Track2) Explaining Machine Learning Predictions: State-of-the-art, Challenges, and Opportunities Q&A »
Himabindu Lakkaraju · Julius Adebayo · Sameer Singh -
2020 Poster: Robust Optimization for Fairness with Noisy Protected Groups »
Serena Wang · Wenshuo Guo · Harikrishna Narasimhan · Andrew Cotter · Maya Gupta · Michael Jordan -
2020 Poster: On the Theory of Transfer Learning: The Importance of Task Diversity »
Nilesh Tripuraneni · Michael Jordan · Chi Jin -
2020 Poster: Improving Local Identifiability in Probabilistic Box Embeddings »
Shib Dasgupta · Michael Boratko · Dongxu Zhang · Luke Vilnis · Xiang Li · Andrew McCallum -
2020 Session: Orals & Spotlights Track 08: Deep Learning »
Graham Taylor · Mario Lucic -
2020 Poster: On Function Approximation in Reinforcement Learning: Optimism in the Face of Large State Spaces »
Zhuoran Yang · Chi Jin · Zhaoran Wang · Mengdi Wang · Michael Jordan -
2020 Tutorial: (Track2) Explaining Machine Learning Predictions: State-of-the-art, Challenges, and Opportunities »
Himabindu Lakkaraju · Julius Adebayo · Sameer Singh -
2019 : Invited Talk - Alexander J. Smola - Sets and symmetries »
Alexander Smola -
2019 : Poster and Coffee Break 2 »
Karol Hausman · Kefan Dong · Ken Goldberg · Lihong Li · Lin Yang · Lingxiao Wang · Lior Shani · Liwei Wang · Loren Amdahl-Culleton · Lucas Cassano · Marc Dymetman · Marc Bellemare · Marcin Tomczak · Margarita Castro · Marius Kloft · Marius-Constantin Dinu · Markus Holzleitner · Martha White · Mengdi Wang · Michael Jordan · Mihailo Jovanovic · Ming Yu · Minshuo Chen · Moonkyung Ryu · Muhammad Zaheer · Naman Agarwal · Nan Jiang · Niao He · Nikolaus Yasui · Nikos Karampatziakis · Nino Vieillard · Ofir Nachum · Olivier Pietquin · Ozan Sener · Pan Xu · Parameswaran Kamalaruban · Paul Mineiro · Paul Rolland · Philip Amortila · Pierre-Luc Bacon · Prakash Panangaden · Qi Cai · Qiang Liu · Quanquan Gu · Raihan Seraj · Richard Sutton · Rick Valenzano · Robert Dadashi · Rodrigo Toro Icarte · Roshan Shariff · Roy Fox · Ruosong Wang · Saeed Ghadimi · Samuel Sokota · Sean Sinclair · Sepp Hochreiter · Sergey Levine · Sergio Valcarcel Macua · Sham Kakade · Shangtong Zhang · Sheila McIlraith · Shie Mannor · Shimon Whiteson · Shuai Li · Shuang Qiu · Wai Lok Li · Siddhartha Banerjee · Sitao Luan · Tamer Basar · Thinh Doan · Tianhe Yu · Tianyi Liu · Tom Zahavy · Toryn Klassen · Tuo Zhao · Vicenç Gómez · Vincent Liu · Volkan Cevher · Wesley Suttle · Xiao-Wen Chang · Xiaohan Wei · Xiaotong Liu · Xingguo Li · Xinyi Chen · Xingyou Song · Yao Liu · YiDing Jiang · Yihao Feng · Yilun Du · Yinlam Chow · Yinyu Ye · Yishay Mansour · · Yonathan Efroni · Yongxin Chen · Yuanhao Wang · Bo Dai · Chen-Yu Wei · Harsh Shrivastava · Hongyang Zhang · Qinqing Zheng · SIDDHARTHA SATPATHI · Xueqing Liu · Andreu Vall -
2019 : Coffee Break & Poster Session 2 »
Juho Lee · Yoonho Lee · Yee Whye Teh · Raymond A. Yeh · Yuan-Ting Hu · Alex Schwing · Sara Ahmadian · Alessandro Epasto · Marina Knittel · Ravi Kumar · Mohammad Mahdian · Christian Bueno · Aditya Sanghi · Pradeep Kumar Jayaraman · Ignacio Arroyo-Fernández · Andrew Hryniowski · Vinayak Mathur · Sanjay Singh · Shahrzad Haddadan · Vasco Portilheiro · Luna Zhang · Mert Yuksekgonul · Jhosimar Arias Figueroa · Deepak Maurya · Balaraman Ravindran · Frank NIELSEN · Philip Pham · Justin Payan · Andrew McCallum · Jinesh Mehta · Ke SUN -
2019 : Opening Remarks »
Manzil Zaheer · Nicholas Monath · Ari Kobren · Junier Oliva · Barnabas Poczos · Ruslan Salakhutdinov · Andrew McCallum -
2019 Workshop: Sets and Partitions »
Nicholas Monath · Manzil Zaheer · Andrew McCallum · Ari Kobren · Junier Oliva · Barnabas Poczos · Ruslan Salakhutdinov -
2019 : Andrew McCallum: Learning DAGs and Trees with Box Embeddings and Hyperbolic Embeddings »
Andrew McCallum -
2019 Workshop: KR2ML - Knowledge Representation and Reasoning Meets Machine Learning »
Veronika Thost · Christian Muise · Kartik Talamadupula · Sameer Singh · Christopher Ré -
2019 Poster: Search-Guided, Lightly-Supervised Training of Structured Prediction Energy Networks »
Amirmohammad Rooshenas · Dongxu Zhang · Gopal Sharma · Andrew McCallum -
2019 Poster: Transferable Normalization: Towards Improving Transferability of Deep Neural Networks »
Ximei Wang · Ying Jin · Mingsheng Long · Jianmin Wang · Michael Jordan -
2019 Poster: Understanding Attention and Generalization in Graph Neural Networks »
Boris Knyazev · Graham Taylor · Mohamed Amer -
2019 Demonstration: AllenNLP Interpret: Explaining Predictions of NLP Models »
Jens Tuyls · Eric Wallace · Matt Gardner · Junlin Wang · Sameer Singh · Sanjay Subramanian -
2019 Poster: Acceleration via Symplectic Discretization of High-Resolution Differential Equations »
Bin Shi · Simon Du · Weijie Su · Michael Jordan -
2018 Poster: Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation »
Kush Bhatia · Aldo Pacchiano · Nicolas Flammarion · Peter Bartlett · Michael Jordan -
2018 Poster: Compact Representation of Uncertainty in Clustering »
Craig Greenberg · Nicholas Monath · Ari Kobren · Patrick Flaherty · Andrew McGregor · Andrew McCallum -
2018 Poster: Theoretical guarantees for EM under misspecified Gaussian mixture models »
Raaz Dwivedi · nhật Hồ · Koulik Khamaru · Martin Wainwright · Michael Jordan -
2018 Poster: Stochastic Cubic Regularization for Fast Nonconvex Optimization »
Nilesh Tripuraneni · Mitchell Stern · Chi Jin · Jeffrey Regier · Michael Jordan -
2018 Poster: On the Local Minima of the Empirical Risk »
Chi Jin · Lydia T. Liu · Rong Ge · Michael Jordan -
2018 Spotlight: On the Local Minima of the Empirical Risk »
Chi Jin · Lydia T. Liu · Rong Ge · Michael Jordan -
2018 Oral: Stochastic Cubic Regularization for Fast Nonconvex Optimization »
Nilesh Tripuraneni · Mitchell Stern · Chi Jin · Jeffrey Regier · Michael Jordan -
2018 Poster: Is Q-Learning Provably Efficient? »
Chi Jin · Zeyuan Allen-Zhu · Sebastien Bubeck · Michael Jordan -
2018 Poster: Learning to Optimize Tensor Programs »
Tianqi Chen · Lianmin Zheng · Eddie Yan · Ziheng Jiang · Thierry Moreau · Luis Ceze · Carlos Guestrin · Arvind Krishnamurthy -
2018 Poster: Information Constraints on Auto-Encoding Variational Bayes »
Romain Lopez · Jeffrey Regier · Michael Jordan · Nir Yosef -
2018 Spotlight: Learning to Optimize Tensor Programs »
Tianqi Chen · Lianmin Zheng · Eddie Yan · Ziheng Jiang · Thierry Moreau · Luis Ceze · Carlos Guestrin · Arvind Krishnamurthy -
2018 Poster: Conditional Adversarial Domain Adaptation »
Mingsheng Long · ZHANGJIE CAO · Jianmin Wang · Michael Jordan -
2018 Poster: Generalized Zero-Shot Learning with Deep Calibration Network »
Shichen Liu · Mingsheng Long · Jianmin Wang · Michael Jordan -
2018 Poster: Training Deep Models Faster with Robust, Approximate Importance Sampling »
Tyler Johnson · Carlos Guestrin -
2017 : From deep learning of disentangled representations to higher-level cognition »
Yoshua Bengio -
2017 : Invited Talk: "Light Supervision of Structured Prediction Energy Networks" »
Andrew McCallum -
2017 : TBA11 »
Alexander Smola -
2017 : Poster spotlights »
Hiroshi Kuwajima · Masayuki Tanaka · Qingkai Liang · Matthieu Komorowski · Fanyu Que · Thalita F Drumond · Aniruddh Raghu · Leo Anthony Celi · Christina Göpfert · Andrew Ross · Sarah Tan · Rich Caruana · Yin Lou · Devinder Kumar · Graham Taylor · Forough Poursabzi-Sangdeh · Jennifer Wortman Vaughan · Hanna Wallach -
2017 Oral: Deep Sets »
Manzil Zaheer · Satwik Kottur · Siamak Ravanbakhsh · Barnabas Poczos · Ruslan Salakhutdinov · Alexander Smola -
2017 Poster: Fast Black-box Variational Inference through Stochastic Trust-Region Optimization »
Jeffrey Regier · Michael Jordan · Jon McAuliffe -
2017 Poster: Deep Sets »
Manzil Zaheer · Satwik Kottur · Siamak Ravanbakhsh · Barnabas Poczos · Ruslan Salakhutdinov · Alexander Smola -
2017 Poster: Online control of the false discovery rate with decaying memory »
Aaditya Ramdas · Fanny Yang · Martin Wainwright · Michael Jordan -
2017 Spotlight: Fast Black-box Variational Inference through Stochastic Trust-Region Optimization »
Jeffrey Regier · Michael Jordan · Jon McAuliffe -
2017 Oral: Online control of the false discovery rate with decaying memory »
Aaditya Ramdas · Fanny Yang · Martin Wainwright · Michael Jordan -
2017 Poster: Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples »
Haw-Shiuan Chang · Erik Learned-Miller · Andrew McCallum -
2017 Poster: Gradient Descent Can Take Exponential Time to Escape Saddle Points »
Simon Du · Chi Jin · Jason D Lee · Michael Jordan · Aarti Singh · Barnabas Poczos -
2017 Spotlight: Gradient Descent Can Take Exponential Time to Escape Saddle Points »
Simon Du · Chi Jin · Jason D Lee · Michael Jordan · Aarti Singh · Barnabas Poczos -
2017 Poster: Non-convex Finite-Sum Optimization Via SCSG Methods »
Lihua Lei · Cheng Ju · Jianbo Chen · Michael Jordan -
2017 Poster: Kernel Feature Selection via Conditional Covariance Minimization »
Jianbo Chen · Mitchell Stern · Martin J Wainwright · Michael Jordan -
2016 : Invited talk, Carlos Guestrin »
Carlos Guestrin -
2016 Workshop: Advances in Approximate Bayesian Inference »
Tamara Broderick · Stephan Mandt · James McInerney · Dustin Tran · David Blei · Kevin Murphy · Andrew Gelman · Michael I Jordan -
2016 Poster: Variance Reduction in Stochastic Gradient Langevin Dynamics »
Kumar Avinava Dubey · Sashank J. Reddi · Sinead Williamson · Barnabas Poczos · Alexander Smola · Eric Xing -
2016 Poster: Cyclades: Conflict-free Asynchronous Machine Learning »
Xinghao Pan · Maximilian Lam · Stephen Tu · Dimitris Papailiopoulos · Ce Zhang · Michael Jordan · Kannan Ramchandran · Christopher Ré · Benjamin Recht -
2016 Poster: Unsupervised Domain Adaptation with Residual Transfer Networks »
Mingsheng Long · Han Zhu · Jianmin Wang · Michael Jordan -
2016 Poster: Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences »
Chi Jin · Yuchen Zhang · Sivaraman Balakrishnan · Martin J Wainwright · Michael Jordan -
2016 Poster: Unified Methods for Exploiting Piecewise Linear Structure in Convex Optimization »
Tyler Johnson · Carlos Guestrin -
2016 Poster: Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization »
Sashank J. Reddi · Suvrit Sra · Barnabas Poczos · Alexander Smola -
2015 : Scaling Machine Learning »
Alexander Smola -
2015 Workshop: Machine Learning Systems »
Alex Beutel · Tianqi Chen · Sameer Singh · Elaine Angelino · Markus Weimer · Joseph Gonzalez -
2015 : Learning Multi-scale Temporal Dynamics with Recurrent Neural Networks »
Graham Taylor -
2015 Workshop: Nonparametric Methods for Large Scale Representation Learning »
Andrew G Wilson · Alexander Smola · Eric Xing -
2015 Poster: Fast and Guaranteed Tensor Decomposition via Sketching »
Yining Wang · Hsiao-Yu Tung · Alexander Smola · Anima Anandkumar -
2015 Spotlight: Fast and Guaranteed Tensor Decomposition via Sketching »
Yining Wang · Hsiao-Yu Tung · Alexander Smola · Anima Anandkumar -
2015 Poster: Variational Consensus Monte Carlo »
Maxim Rabinovich · Elaine Angelino · Michael Jordan -
2015 Poster: On the Accuracy of Self-Normalized Log-Linear Models »
Jacob Andreas · Maxim Rabinovich · Michael Jordan · Dan Klein -
2015 Poster: On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants »
Sashank J. Reddi · Ahmed Hefny · Suvrit Sra · Barnabas Poczos · Alexander Smola -
2015 Poster: Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes »
Ryan Giordano · Tamara Broderick · Michael Jordan -
2015 Spotlight: Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes »
Ryan Giordano · Tamara Broderick · Michael Jordan -
2015 Tutorial: Deep Learning »
Geoffrey E Hinton · Yoshua Bengio · Yann LeCun -
2014 Workshop: Advances in Variational Inference »
David Blei · Shakir Mohamed · Michael Jordan · Charles Blundell · Tamara Broderick · Matthew D. Hoffman -
2014 Workshop: Second Workshop on Transfer and Multi-Task Learning: Theory meets Practice »
Urun Dogan · Tatiana Tommasi · Yoshua Bengio · Francesco Orabona · Marius Kloft · Andres Munoz · Gunnar Rätsch · Hal Daumé III · Mehryar Mohri · Xuezhi Wang · Daniel Hernández-lobato · Song Liu · Thomas Unterthiner · Pascal Germain · Vinay P Namboodiri · Michael Goetz · Christopher Berlind · Sigurd Spieckermann · Marta Soare · Yujia Li · Vitaly Kuznetsov · Wenzhao Lian · Daniele Calandriello · Emilie Morvant -
2014 Workshop: 3rd NIPS Workshop on Probabilistic Programming »
Daniel Roy · Josh Tenenbaum · Thomas Dietterich · Stuart J Russell · YI WU · Ulrik R Beierholm · Alp Kucukelbir · Zenna Tavares · Yura Perov · Daniel Lee · Brian Ruttenberg · Sameer Singh · Michael Hughes · Marco Gaboardi · Alexey Radul · Vikash Mansinghka · Frank Wood · Sebastian Riedel · Prakash Panangaden -
2014 Workshop: 4th Workshop on Automated Knowledge Base Construction (AKBC) »
Sameer Singh · Fabian M Suchanek · Sebastian Riedel · Partha Pratim Talukdar · Kevin Murphy · Christopher Ré · William Cohen · Tom Mitchell · Andrew McCallum · Jason E Weston · Ramanathan Guha · Boyan Onyshkevych · Hoifung Poon · Oren Etzioni · Ari Kobren · Arvind Neelakantan · Peter Clark -
2014 Workshop: Deep Learning and Representation Learning »
Andrew Y Ng · Yoshua Bengio · Adam Coates · Roland Memisevic · Sharanyan Chetlur · Geoffrey E Hinton · Shamim Nemati · Bryan Catanzaro · Surya Ganguli · Herbert Jaeger · Phil Blunsom · Leon Bottou · Volodymyr Mnih · Chen-Yu Lee · Rich M Schwartz -
2014 Workshop: OPT2014: Optimization for Machine Learning »
Zaid Harchaoui · Suvrit Sra · Alekh Agarwal · Martin Jaggi · Miro Dudik · Aaditya Ramdas · Jean Lasserre · Yoshua Bengio · Amir Beck -
2014 Poster: Communication Efficient Distributed Machine Learning with the Parameter Server »
Mu Li · David G Andersen · Alexander Smola · Kai Yu -
2014 Poster: Communication-Efficient Distributed Dual Coordinate Ascent »
Martin Jaggi · Virginia Smith · Martin Takac · Jonathan Terhorst · Sanjay Krishnan · Thomas Hofmann · Michael Jordan -
2014 Poster: Divide-and-Conquer Learning by Anchoring a Conical Hull »
Tianyi Zhou · Jeffrey A Bilmes · Carlos Guestrin -
2014 Poster: How transferable are features in deep neural networks? »
Jason Yosinski · Jeff Clune · Yoshua Bengio · Hod Lipson -
2014 Poster: Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcing »
Yuchen Zhang · Xi Chen · Denny Zhou · Michael Jordan -
2014 Poster: Identifying and attacking the saddle point problem in high-dimensional non-convex optimization »
Yann N Dauphin · Razvan Pascanu · Caglar Gulcehre · Kyunghyun Cho · Surya Ganguli · Yoshua Bengio -
2014 Poster: Parallel Double Greedy Submodular Maximization »
Xinghao Pan · Stefanie Jegelka · Joseph Gonzalez · Joseph K Bradley · Michael Jordan -
2014 Poster: Spectral Methods for Indian Buffet Process Inference »
Hsiao-Yu Tung · Alexander Smola -
2014 Demonstration: A Visual and Interactive IDE for Probabilistic Programming »
Sameer Singh · Luke Hewitt · Tim Rocktäschel · Sebastian Riedel -
2014 Spotlight: Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcing »
Yuchen Zhang · Xi Chen · Denny Zhou · Michael Jordan -
2014 Poster: Generative Adversarial Nets »
Ian Goodfellow · Jean Pouget-Abadie · Mehdi Mirza · Bing Xu · David Warde-Farley · Sherjil Ozair · Aaron Courville · Yoshua Bengio -
2014 Poster: On the Number of Linear Regions of Deep Neural Networks »
Guido F Montufar · Razvan Pascanu · Kyunghyun Cho · Yoshua Bengio -
2014 Demonstration: Neural Machine Translation »
Bart van Merriënboer · Kyunghyun Cho · Dzmitry Bahdanau · Yoshua Bengio -
2014 Oral: How transferable are features in deep neural networks? »
Jason Yosinski · Jeff Clune · Yoshua Bengio · Hod Lipson -
2014 Poster: Iterative Neural Autoregressive Distribution Estimator NADE-k »
Tapani Raiko · Yao Li · Kyunghyun Cho · Yoshua Bengio -
2014 Poster: On the Convergence Rate of Decomposable Submodular Function Minimization »
Robert Nishihara · Stefanie Jegelka · Michael Jordan -
2013 Workshop: Topic Models: Computation, Application, and Evaluation »
David Mimno · Amr Ahmed · Jordan Boyd-Graber · Ankur Moitra · Hanna Wallach · Alexander Smola · David Blei · Anima Anandkumar -
2013 Workshop: Big Learning : Advances in Algorithms and Data Management »
Xinghao Pan · Haijie Gu · Joseph Gonzalez · Sameer Singh · Yucheng Low · Joseph Hellerstein · Derek G Murray · Raghu Ramakrishnan · Michael Jordan · Christopher Ré -
2013 Workshop: Randomized Methods for Machine Learning »
David Lopez-Paz · Quoc V Le · Alexander Smola -
2013 Workshop: Deep Learning »
Yoshua Bengio · Hugo Larochelle · Russ Salakhutdinov · Tomas Mikolov · Matthew D Zeiler · David Mcallester · Nando de Freitas · Josh Tenenbaum · Jian Zhou · Volodymyr Mnih -
2013 Workshop: Discrete Optimization in Machine Learning: Connecting Theory and Practice »
Stefanie Jegelka · Andreas Krause · Pradeep Ravikumar · Kazuo Murota · Jeffrey A Bilmes · Yisong Yue · Michael Jordan -
2013 Workshop: Output Representation Learning »
Yuhong Guo · Dale Schuurmans · Richard Zemel · Samy Bengio · Yoshua Bengio · Li Deng · Dan Roth · Kilian Q Weinberger · Jason Weston · Kihyuk Sohn · Florent Perronnin · Gabriel Synnaeve · Pablo R Strasser · julien audiffren · Carlo Ciliberto · Dan Goldwasser -
2013 Workshop: Modern Nonparametric Methods in Machine Learning »
Arthur Gretton · Mladen Kolar · Samory Kpotufe · John Lafferty · Han Liu · Bernhard Schölkopf · Alexander Smola · Rob Nowak · Mikhail Belkin · Lorenzo Rosasco · peter bickel · Yue Zhao -
2013 Session: Oral Session 10 »
Michael Jordan -
2013 Poster: A Comparative Framework for Preconditioned Lasso Algorithms »
Fabian L Wauthier · Nebojsa Jojic · Michael Jordan -
2013 Poster: Information-theoretic lower bounds for distributed statistical estimation with communication constraints »
Yuchen Zhang · John Duchi · Michael Jordan · Martin J Wainwright -
2013 Oral: Information-theoretic lower bounds for distributed statistical estimation with communication constraints »
Yuchen Zhang · John Duchi · Michael Jordan · Martin J Wainwright -
2013 Poster: Variance Reduction for Stochastic Gradient Optimization »
Chong Wang · Xi Chen · Alexander Smola · Eric Xing -
2013 Poster: Multi-Prediction Deep Boltzmann Machines »
Ian Goodfellow · Mehdi Mirza · Aaron Courville · Yoshua Bengio -
2013 Poster: Generalized Denoising Auto-Encoders as Generative Models »
Yoshua Bengio · Li Yao · Guillaume Alain · Pascal Vincent -
2013 Poster: Stochastic Ratio Matching of RBMs for Sparse High-Dimensional Inputs »
Yann Dauphin · Yoshua Bengio -
2013 Poster: Optimistic Concurrency Control for Distributed Unsupervised Learning »
Xinghao Pan · Joseph Gonzalez · Stefanie Jegelka · Tamara Broderick · Michael Jordan -
2013 Poster: Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation »
John Duchi · Martin J Wainwright · Michael Jordan -
2013 Poster: Streaming Variational Bayes »
Tamara Broderick · Nicholas Boyd · Andre Wibisono · Ashia C Wilson · Michael Jordan -
2013 Poster: Estimation, Optimization, and Parallelism when Data is Sparse »
John Duchi · Michael Jordan · Brendan McMahan -
2012 Workshop: Bayesian Nonparametric Models For Reliable Planning And Decision-Making Under Uncertainty »
Jonathan How · Lawrence Carin · John Fisher III · Michael Jordan · Alborz Geramifard -
2012 Workshop: Deep Learning and Unsupervised Feature Learning »
Yoshua Bengio · James Bergstra · Quoc V. Le -
2012 Workshop: Confluence between Kernel Methods and Graphical Models »
Le Song · Arthur Gretton · Alexander Smola -
2012 Workshop: Big Learning : Algorithms, Systems, and Tools »
Sameer Singh · John Duchi · Yucheng Low · Joseph E Gonzalez -
2012 Session: Oral Session 10 »
Alexander Smola -
2012 Poster: Learning Networks of Heterogeneous Influence »
Nan Du · Le Song · Alexander Smola · Ming Yuan -
2012 Poster: FastEx: Fast Clustering with Exponential Families »
Amr Ahmed · Sujith Ravi · Shravan M Narayanamurthy · Alexander Smola -
2012 Poster: Privacy Aware Learning »
John Duchi · Michael Jordan · Martin J Wainwright -
2012 Poster: Ancestor Sampling for Particle Gibbs »
Fredrik Lindsten · Michael Jordan · Thomas Schön -
2012 Demonstration: GraphLab: A Framework For Machine Learning in the Cloud »
Yucheng Low · Haijie Gu · Carlos Guestrin -
2012 Oral: Privacy Aware Learning »
John Duchi · Michael Jordan · Martin J Wainwright -
2012 Spotlight: Learning Networks of Heterogeneous Influence »
Nan Du · Le Song · Alexander Smola · Ming Yuan -
2012 Poster: Finite Sample Convergence Rates of Zero-Order Stochastic Optimization Methods »
John Duchi · Michael Jordan · Martin J Wainwright · Andre Wibisono -
2012 Poster: Small-Variance Asymptotics for Exponential Family Dirichlet Process Mixture Models »
Ke Jiang · Brian Kulis · Michael Jordan -
2012 Poster: MAP Inference in Chains using Column Generation »
David Belanger · Alexandre T Passos · Sebastian Riedel · Andrew McCallum -
2011 Workshop: Deep Learning and Unsupervised Feature Learning »
Yoshua Bengio · Adam Coates · Yann LeCun · Nicolas Le Roux · Andrew Y Ng -
2011 Oral: The Manifold Tangent Classifier »
Salah Rifai · Yann N Dauphin · Pascal Vincent · Yoshua Bengio · Xavier Muller -
2011 Poster: Bayesian Bias Mitigation for Crowdsourcing »
Fabian L Wauthier · Michael Jordan -
2011 Poster: Divide-and-Conquer Matrix Factorization »
Lester W Mackey · Ameet S Talwalkar · Michael Jordan -
2011 Poster: Query-Aware MCMC »
Michael Wick · Andrew McCallum -
2011 Poster: Shallow vs. Deep Sum-Product Networks »
Olivier Delalleau · Yoshua Bengio -
2011 Poster: The Manifold Tangent Classifier »
Salah Rifai · Yann N Dauphin · Pascal Vincent · Yoshua Bengio · Xavier Muller -
2011 Poster: Linear Submodular Bandits and their Application to Diversified Retrieval »
Yisong Yue · Carlos Guestrin -
2011 Poster: Algorithms for Hyper-Parameter Optimization »
James Bergstra · Rémi Bardenet · Yoshua Bengio · Balázs Kégl -
2011 Poster: Facial Expression Transfer with Input-Output Temporal Restricted Boltzmann Machines »
Matthew D Zeiler · Graham Taylor · Leonid Sigal · Iain Matthews · Rob Fergus -
2011 Poster: On Tracking The Partition Function »
Guillaume Desjardins · Aaron Courville · Yoshua Bengio -
2011 Tutorial: Graphical Models for the Internet »
Amr Ahmed · Alexander Smola -
2010 Workshop: Challenges of Data Visualization »
Barbara Hammer · Laurens van der Maaten · Fei Sha · Alexander Smola -
2010 Workshop: Deep Learning and Unsupervised Feature Learning »
Honglak Lee · Marc'Aurelio Ranzato · Yoshua Bengio · Geoffrey E Hinton · Yann LeCun · Andrew Y Ng -
2010 Oral: Tree-Structured Stick Breaking for Hierarchical Data »
Ryan Adams · Zoubin Ghahramani · Michael Jordan -
2010 Invited Talk: Statistical Inference of Protein Structure and Function »
Michael Jordan -
2010 Poster: Tree-Structured Stick Breaking for Hierarchical Data »
Ryan Adams · Zoubin Ghahramani · Michael Jordan -
2010 Poster: Word Features for Latent Dirichlet Allocation »
James Petterson · Alexander Smola · Tiberio Caetano · Wray L Buntine · Shravan M Narayanamurthy -
2010 Poster: Evidence-Specific Structures for Rich Tractable CRFs »
Anton Chechetka · Carlos Guestrin -
2010 Spotlight: Variational Inference over Combinatorial Spaces »
Alexandre Bouchard-Côté · Michael Jordan -
2010 Poster: Variational Inference over Combinatorial Spaces »
Alexandre Bouchard-Côté · Michael Jordan -
2010 Poster: Unsupervised Kernel Dimension Reduction »
Meihong Wang · Fei Sha · Michael Jordan -
2010 Poster: Pose-Sensitive Embedding by Nonlinear NCA Regression »
Graham Taylor · Rob Fergus · George Williams · Ian Spiro · Christoph Bregler -
2010 Poster: Optimal Web-Scale Tiering as a Flow Problem »
Gilbert Leung · Novi Quadrianto · Alexander Smola · Kostas Tsioutsiouliklis -
2010 Poster: Inference with Multivariate Heavy-Tails in Linear Models »
Danny Bickson · Carlos Guestrin -
2010 Poster: Heavy-Tailed Process Priors for Selective Shrinkage »
Fabian L Wauthier · Michael Jordan -
2010 Poster: Multitask Learning without Label Correspondences »
Novi Quadrianto · Alexander Smola · Tiberio Caetano · S.V.N. Vishwanathan · James Petterson -
2010 Poster: Parallelized Stochastic Gradient Descent »
Martin A Zinkevich · Markus Weimer · Alexander Smola · Lihong Li -
2010 Poster: Random Conic Pursuit for Semidefinite Programming »
Ariel Kleiner · ali rahimi · Michael Jordan -
2009 Workshop: Nonparametric Bayes »
Dilan Gorur · Francois Caron · Yee Whye Teh · David B Dunson · Zoubin Ghahramani · Michael Jordan -
2009 Workshop: Learning with Orderings »
Tiberio Caetano · Carlos Guestrin · Jonathan Huang · Risi Kondor · Guy Lebanon · Marina Meila -
2009 Workshop: Large-Scale Machine Learning: Parallelism and Massive Datasets »
Alexander Gray · Arthur Gretton · Alexander Smola · Joseph E Gonzalez · Carlos Guestrin -
2009 Poster: Sharing Features among Dynamical Systems with Beta Processes »
Emily Fox · Erik Sudderth · Michael Jordan · Alan S Willsky -
2009 Poster: FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs »
Andrew McCallum · Karl Schultz · Sameer Singh -
2009 Poster: Slow, Decorrelated Features for Pretraining Complex Cell-like Networks »
James Bergstra · Yoshua Bengio -
2009 Poster: An Infinite Factor Model Hierarchy Via a Noisy-Or Mechanism »
Aaron Courville · Douglas Eck · Yoshua Bengio -
2009 Oral: Sharing Features among Dynamical Systems with Beta Processes »
Emily Fox · Erik Sudderth · Michael Jordan · Alan S Willsky -
2009 Poster: Training Factor Graphs with Reinforcement Learning for Efficient MAP Inference »
Michael Wick · Khashayar Rohanimanesh · Sameer Singh · Andrew McCallum -
2009 Poster: Slow Learners are Fast »
Martin A Zinkevich · Alexander Smola · John Langford -
2009 Poster: Riffled Independence for Ranked Data »
Jonathan Huang · Carlos Guestrin -
2009 Spotlight: Riffled Independence for Ranked Data »
Jonathan Huang · Carlos Guestrin -
2009 Spotlight: Training Factor Graphs with Reinforcement Learning for Efficient MAP Inference »
Michael Wick · Khashayar Rohanimanesh · Sameer Singh · Andrew McCallum -
2009 Session: Debate on Future Publication Models for the NIPS Community »
Yoshua Bengio -
2009 Poster: Distribution Matching for Transduction »
Novi Quadrianto · James Petterson · Alexander Smola -
2009 Poster: Asymptotically Optimal Regularization in Smooth Parametric Models »
Percy Liang · Francis Bach · Guillaume Bouchard · Michael Jordan -
2009 Poster: Rethinking LDA: Why Priors Matter »
Hanna Wallach · David Mimno · Andrew McCallum -
2009 Poster: Nonparametric Latent Feature Models for Link Prediction »
Kurt T Miller · Tom Griffiths · Michael Jordan -
2009 Spotlight: Nonparametric Latent Feature Models for Link Prediction »
Kurt T Miller · Tom Griffiths · Michael Jordan -
2009 Spotlight: Rethinking LDA: Why Priors Matter »
Hanna Wallach · David Mimno · Andrew McCallum -
2008 Oral: Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes »
Erik Sudderth · Michael Jordan -
2008 Poster: Nonparametric Bayesian Learning of Switching Linear Dynamical Systems »
Emily Fox · Erik Sudderth · Michael Jordan · Alan S Willsky -
2008 Poster: Kernelized Sorting »
Novi Quadrianto · Le Song · Alexander Smola -
2008 Poster: Kernel Measures of Independence for non-iid Data »
Xinhua Zhang · Le Song · Arthur Gretton · Alexander Smola -
2008 Poster: High-dimensional union support recovery in multivariate regression »
Guillaume R Obozinski · Martin J Wainwright · Michael Jordan -
2008 Poster: Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes »
Erik Sudderth · Michael Jordan -
2008 Spotlight: High-dimensional union support recovery in multivariate regression »
Guillaume R Obozinski · Martin J Wainwright · Michael Jordan -
2008 Spotlight: Kernelized Sorting »
Novi Quadrianto · Le Song · Alexander Smola -
2008 Spotlight: Kernel Measures of Independence for non-iid Data »
Xinhua Zhang · Le Song · Arthur Gretton · Alexander Smola -
2008 Spotlight: Nonparametric Bayesian Learning of Switching Linear Dynamical Systems »
Emily Fox · Erik Sudderth · Michael Jordan · Alan S Willsky -
2008 Poster: Posterior Consistency of the Silverman g-prior in Bayesian Model Choice »
Zhihua Zhang · Michael Jordan · Dit-Yan Yeung -
2008 Poster: The Recurrent Temporal Restricted Boltzmann Machine »
Ilya Sutskever · Geoffrey E Hinton · Graham Taylor -
2008 Poster: Tighter Bounds for Structured Estimation »
Olivier Chapelle · Chuong B Do · Quoc V Le · Alexander Smola · Choon Hui Teo -
2008 Poster: DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification »
Simon Lacoste-Julien · Fei Sha · Michael Jordan -
2008 Spotlight: Posterior Consistency of the Silverman g-prior in Bayesian Model Choice »
Zhihua Zhang · Michael Jordan · Dit-Yan Yeung -
2008 Poster: Efficient Inference in Phylogenetic InDel Trees »
Alexandre Bouchard-Côté · Michael Jordan · Dan Klein -
2008 Poster: Spectral Clustering with Perturbed Data »
Ling Huang · Donghui Yan · Michael Jordan · Nina Taft -
2008 Poster: Robust Near-Isometric Matching via Structured Learning of Graphical Models »
Julian J McAuley · Tiberio Caetano · Alexander Smola -
2008 Spotlight: Efficient Inference in Phylogenetic InDel Trees »
Alexandre Bouchard-Côté · Michael Jordan · Dan Klein -
2008 Spotlight: Spectral Clustering with Perturbed Data »
Ling Huang · Donghui Yan · Michael Jordan · Nina Taft -
2007 Workshop: Representations and Inference on Probability Distributions »
Kenji Fukumizu · Arthur Gretton · Alexander Smola -
2007 Oral: Efficient Inference forDistributions on Permutations »
Jonathan Huang · Carlos Guestrin · Leonidas Guibas -
2007 Poster: Augmented Functional Time Series Representation and Forecasting with Gaussian Processes »
Nicolas Chapados · Yoshua Bengio -
2007 Poster: Agreement-Based Learning »
Percy Liang · Dan Klein · Michael Jordan -
2007 Poster: Efficient Inference forDistributions on Permutations »
Jonathan Huang · Carlos Guestrin · Leonidas Guibas -
2007 Poster: Convex Learning with Invariances »
Choon Hui Teo · Amir Globerson · Sam T Roweis · Alexander Smola -
2007 Poster: Learning the 2-D Topology of Images »
Nicolas Le Roux · Yoshua Bengio · Pascal Lamblin · Marc Joliveau · Balázs Kégl -
2007 Spotlight: Augmented Functional Time Series Representation and Forecasting with Gaussian Processes »
Nicolas Chapados · Yoshua Bengio -
2007 Spotlight: Agreement-Based Learning »
Percy Liang · Dan Klein · Michael Jordan -
2007 Spotlight: Selecting Observations against Adversarial Objectives »
Andreas Krause · H. Brendan McMahan · Carlos Guestrin · Anupam Gupta -
2007 Spotlight: A Kernel Statistical Test of Independence »
Arthur Gretton · Kenji Fukumizu · Choon Hui Teo · Le Song · Bernhard Schölkopf · Alexander Smola -
2007 Spotlight: Bundle Methods for Machine Learning »
Alexander Smola · Vishwanathan S V N · Quoc V Le -
2007 Poster: COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking »
Markus Weimer · Alexandros Karatzoglou · Quoc V Le · Alexander Smola -
2007 Oral: Colored Maximum Variance Unfolding »
Le Song · Alexander Smola · Karsten Borgwardt · Arthur Gretton -
2007 Poster: Colored Maximum Variance Unfolding »
Le Song · Alexander Smola · Karsten Borgwardt · Arthur Gretton -
2007 Poster: A Kernel Statistical Test of Independence »
Arthur Gretton · Kenji Fukumizu · Choon Hui Teo · Le Song · Bernhard Schölkopf · Alexander Smola -
2007 Poster: Bundle Methods for Machine Learning »
Alexander Smola · Vishwanathan S V N · Quoc V Le -
2007 Poster: Selecting Observations against Adversarial Objectives »
Andreas Krause · H. Brendan McMahan · Carlos Guestrin · Anupam Gupta -
2007 Spotlight: COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking »
Markus Weimer · Alexandros Karatzoglou · Quoc V Le · Alexander Smola -
2007 Demonstration: Elefant »
Kishor Gawande · Alexander Smola · Vishwanathan S V N · Li Cheng · Simon A Guenter -
2007 Spotlight: Resampling Methods for Protein Structure Prediction with Rosetta »
Ben Blum · David Baker · Michael Jordan · Philip Bradley · Rhiju Das · David Kim -
2007 Spotlight: Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization »
XuanLong Nguyen · Martin J Wainwright · Michael Jordan -
2007 Spotlight: Convex Learning with Invariances »
Choon Hui Teo · Amir Globerson · Sam T Roweis · Alexander Smola -
2007 Poster: Resampling Methods for Protein Structure Prediction with Rosetta »
Ben Blum · David Baker · Michael Jordan · Philip Bradley · Rhiju Das · David Kim -
2007 Poster: Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization »
XuanLong Nguyen · Martin J Wainwright · Michael Jordan -
2007 Poster: Topmoumoute Online Natural Gradient Algorithm »
Nicolas Le Roux · Pierre-Antoine Manzagol · Yoshua Bengio -
2007 Poster: Efficient Principled Learning of Thin Junction Trees »
Anton Chechetka · Carlos Guestrin -
2006 Poster: Distributed PCA and Network Anomaly Detection »
Ling Huang · XuanLong Nguyen · Minos Garofalakis · Michael Jordan · Anthony D Joseph · Nina Taft -
2006 Poster: A Kernel Method for the Two-Sample-Problem »
Arthur Gretton · Karsten Borgwardt · Malte J Rasch · Bernhard Schölkopf · Alexander Smola -
2006 Poster: Modeling Human Motion Using Binary Latent Variables »
Graham Taylor · Geoffrey E Hinton · Sam T Roweis -
2006 Poster: Greedy Layer-Wise Training of Deep Networks »
Yoshua Bengio · Pascal Lamblin · Dan Popovici · Hugo Larochelle -
2006 Poster: Correcting Sample Selection Bias by Unlabeled Data »
Jiayuan Huang · Alexander Smola · Arthur Gretton · Karsten Borgwardt · Bernhard Schölkopf -
2006 Spotlight: Correcting Sample Selection Bias by Unlabeled Data »
Jiayuan Huang · Alexander Smola · Arthur Gretton · Karsten Borgwardt · Bernhard Schölkopf -
2006 Talk: A Kernel Method for the Two-Sample-Problem »
Arthur Gretton · Karsten Borgwardt · Malte J Rasch · Bernhard Schölkopf · Alexander Smola -
2006 Talk: Greedy Layer-Wise Training of Deep Networks »
Yoshua Bengio · Pascal Lamblin · Dan Popovici · Hugo Larochelle -
2006 Spotlight: Modeling Human Motion Using Binary Latent Variables »
Graham Taylor · Geoffrey E Hinton · Sam T Roweis -
2006 Poster: Distributed Inference in Dynamical Systems »
Stanislav Funiak · Carlos Guestrin · Mark A Paskin · Rahul Sukthankar