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Author Information
Rahul Mehta (Palantir Technologies)
Andrew Lampinen (Stanford University)
Binghong Chen (Georgia Institute of Technology)
Sergio Pascual-Diaz (PROWLER.io)
Jordi Grau-Moya (PROWLER.io)
Aldo Faisal (Imperial College London)
Jonathan Tompson (Google Brain)
Yiren Lu (Google)
Khimya Khetarpal (Mila- McGill University)
Martin Klissarov (McGill University)
Pierre-Luc Bacon (Stanford University)
Doina Precup (McGill University / Mila / DeepMind Montreal)
Thanard Kurutach (University of California Berkeley)
Aviv Tamar (UC Berkeley)
Pieter Abbeel (UC Berkeley & covariant.ai)
Pieter Abbeel is Professor and Director of the Robot Learning Lab at UC Berkeley [2008- ], Co-Director of the Berkeley AI Research (BAIR) Lab, Co-Founder of covariant.ai [2017- ], Co-Founder of Gradescope [2014- ], Advisor to OpenAI, Founding Faculty Partner AI@TheHouse venture fund, Advisor to many AI/Robotics start-ups. He works in machine learning and robotics. In particular his research focuses on making robots learn from people (apprenticeship learning), how to make robots learn through their own trial and error (reinforcement learning), and how to speed up skill acquisition through learning-to-learn (meta-learning). His robots have learned advanced helicopter aerobatics, knot-tying, basic assembly, organizing laundry, locomotion, and vision-based robotic manipulation. He has won numerous awards, including best paper awards at ICML, NIPS and ICRA, early career awards from NSF, Darpa, ONR, AFOSR, Sloan, TR35, IEEE, and the Presidential Early Career Award for Scientists and Engineers (PECASE). Pieter's work is frequently featured in the popular press, including New York Times, BBC, Bloomberg, Wall Street Journal, Wired, Forbes, Tech Review, NPR.
Jinke He (Delft University of Technology)
Maximilian Igl (University of Oxford)
Shimon Whiteson (University of Oxford)
Wendelin Boehmer (University of Oxford)
Raphaël Marinier (Google)
Olivier Pietquin (Google Research Brain Team)
Karol Hausman (Google Brain)
Sergey Levine (UC Berkeley)

Sergey Levine received a BS and MS in Computer Science from Stanford University in 2009, and a Ph.D. in Computer Science from Stanford University in 2014. He joined the faculty of the Department of Electrical Engineering and Computer Sciences at UC Berkeley in fall 2016. His work focuses on machine learning for decision making and control, with an emphasis on deep learning and reinforcement learning algorithms. Applications of his work include autonomous robots and vehicles, as well as applications in other decision-making domains. His research includes developing algorithms for end-to-end training of deep neural network policies that combine perception and control, scalable algorithms for inverse reinforcement learning, deep reinforcement learning algorithms, and more
Chelsea Finn (Stanford)
Tianhe Yu (Stanford University)
Lisa Lee (Carnegie Mellon University)
Benjamin Eysenbach (Carnegie Mellon University)

Assistant professor at Princeton working on self-supervised reinforcement learning (scaling, algorithms, theory, and applications).
Emilio Parisotto (Carnegie Mellon University)
Eric Xing (Petuum Inc. / Carnegie Mellon University)
Ruslan Salakhutdinov (Carnegie Mellon University)
Hongyu Ren (Stanford University)
Anima Anandkumar (NVIDIA / Caltech)
Anima Anandkumar is a Bren professor at Caltech CMS department and a director of machine learning research at NVIDIA. Her research spans both theoretical and practical aspects of large-scale machine learning. In particular, she has spearheaded research in tensor-algebraic methods, non-convex optimization, probabilistic models and deep learning. Anima is the recipient of several awards and honors such as the Bren named chair professorship at Caltech, Alfred. P. Sloan Fellowship, Young investigator awards from the Air Force and Army research offices, Faculty fellowships from Microsoft, Google and Adobe, and several best paper awards. Anima received her B.Tech in Electrical Engineering from IIT Madras in 2004 and her PhD from Cornell University in 2009. She was a postdoctoral researcher at MIT from 2009 to 2010, a visiting researcher at Microsoft Research New England in 2012 and 2014, an assistant professor at U.C. Irvine between 2010 and 2016, an associate professor at U.C. Irvine between 2016 and 2017 and a principal scientist at Amazon Web Services between 2016 and 2018.
Deepak Pathak (UC Berkeley)
https://github.com/pathak22
Christopher Lu (UC Berkeley and Covariant.ai)
Trevor Darrell (UC Berkeley)
Alexei Efros (UC Berkeley)
Phillip Isola (Massachusetts Institute of Technology)
Feng Liu (University of Technology Sydney)
Bo Han (RIKEN)
Gang Niu (RIKEN)

Gang Niu is currently an indefinite-term senior research scientist at RIKEN Center for Advanced Intelligence Project.
Masashi Sugiyama (RIKEN / University of Tokyo)
Saurabh Kumar (Stanford University)
Janith Petangoda (Imperial College London)
Johan Ferret (Google Brain)
James McClelland (Stanford University and DeepMind)
Kara Liu (University of California, Berkeley)
Animesh Garg (University of Toronto, Vector Institute)
I am a CIFAR AI Chair Assistant Professor of Computer Science at the University of Toronto, a Faculty Member at the Vector Institute, and Sr. Researcher at Nvidia. My current research focuses on machine learning for perception and control in robotics.
Robert Lange (Einstein Center for Neurosciences)
I have finished my undergraduate studies in economics at the University of Cologne. During that time I have worked as a student research assistant for Prof. Alex Ludwig (Goethe University Frankfurt) and Prof. Helge Braun (University of Cologne). The projects mainly focused on public policy evaluation and the intersection of retirement and unemployment insurance systems. I developed a fascination for data wrangling and the computational aspects of Economics. Since September I am part of the 2017 cohort of the Data Science Master's Program at the Barcelona Graduate School of Economics. I am fully convinced that the intersection between behavioral sciences and statistical learning is crucial in order to improve almost every aspect of our lives. Therefore, I am looking forward to pursuing a second master's degree in the are of cognitive sciences and artificial intelligence coming next fall. At the moment my interest focuses on computational statistics and machine learning, interdisciplinary applications such as cognitive sciences, biometrics and the philosophy of risk.
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2022 : DensePure: Understanding Diffusion Models towards Adversarial Robustness »
Zhongzhu Chen · Kun Jin · Jiongxiao Wang · Weili Nie · Mingyan Liu · Anima Anandkumar · Bo Li · Dawn Song -
2022 : Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement »
Michael Chang · Alyssa L Dayan · Franziska Meier · Tom Griffiths · Sergey Levine · Amy Zhang -
2022 : HEAT: Hardware-Efficient Automatic Tensor Decomposition for Transformer Compression »
Jiaqi Gu · Ben Keller · Jean Kossaifi · Anima Anandkumar · Brucek Khailany · David Pan -
2022 : Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement »
Michael Chang · Alyssa L Dayan · Franziska Meier · Tom Griffiths · Sergey Levine · Amy Zhang -
2022 : Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Networks »
Sitao Luan · Mingde Zhao · Chenqing Hua · Xiao-Wen Chang · Doina Precup -
2022 : Studying Bias in GANs through the Lens of Race »
Vongani Maluleke · Neerja Thakkar · Tim Brooks · Ethan Weber · Trevor Darrell · Alexei Efros · Angjoo Kanazawa · Devin Guillory -
2022 : Recommendation for New Drugs with Limited Prescription Data »
Zhenbang Wu · Huaxiu Yao · Zhe Su · David Liebovitz · Lucas Glass · James Zou · Chelsea Finn · Jimeng Sun -
2022 : CLUTR: Curriculum Learning via Unsupervised Task Representation Learning »
Abdus Salam Azad · Izzeddin Gur · Aleksandra Faust · Pieter Abbeel · Ion Stoica -
2022 : What Makes Certain Pre-Trained Visual Representations Better for Robotic Learning? »
Kyle Hsu · Tyler Lum · Ruohan Gao · Shixiang (Shane) Gu · Jiajun Wu · Chelsea Finn -
2022 : Train Offline, Test Online: A Real Robot Learning Benchmark »
Gaoyue Zhou · Victoria Dean · Mohan Kumar Srirama · Aravind Rajeswaran · Jyothish Pari · Kyle Hatch · Aryan Jain · Tianhe Yu · Pieter Abbeel · Lerrel Pinto · Chelsea Finn · Abhinav Gupta -
2022 : Confidence-Conditioned Value Functions for Offline Reinforcement Learning »
Joey Hong · Aviral Kumar · Sergey Levine -
2022 : Bayesian Q-learning With Imperfect Expert Demonstrations »
Fengdi Che · Xiru Zhu · Doina Precup · David Meger · Gregory Dudek -
2022 : Efficient Deep Reinforcement Learning Requires Regulating Statistical Overfitting »
Qiyang Li · Aviral Kumar · Ilya Kostrikov · Sergey Levine -
2022 : Pre-Training for Robots: Leveraging Diverse Multitask Data via Offline Reinforcement Learning »
Anikait Singh · Aviral Kumar · Frederik Ebert · Yanlai Yang · Chelsea Finn · Sergey Levine -
2022 : Offline Reinforcement Learning from Heteroskedastic Data Via Support Constraints »
Anikait Singh · Aviral Kumar · Quan Vuong · Yevgen Chebotar · Sergey Levine -
2022 : Fine-tuning Offline Policies with Optimistic Action Selection »
Max Sobol Mark · Ali Ghadirzadeh · Xi Chen · Chelsea Finn -
2022 : Policy Architectures for Compositional Generalization in Control »
Allan Zhou · Vikash Kumar · Chelsea Finn · Aravind Rajeswaran -
2022 : Sample-Efficient Reinforcement Learning by Breaking the Replay Ratio Barrier »
Pierluca D'Oro · Max Schwarzer · Evgenii Nikishin · Pierre-Luc Bacon · Marc Bellemare · Aaron Courville -
2022 : Adversarial Policies Beat Professional-Level Go AIs »
Tony Wang · Adam Gleave · Nora Belrose · Tom Tseng · Michael Dennis · Yawen Duan · Viktor Pogrebniak · Joseph Miller · Sergey Levine · Stuart J Russell -
2022 : Contrastive Example-Based Control »
Kyle Hatch · Sarthak J Shetty · Benjamin Eysenbach · Tianhe Yu · Rafael Rafailov · Russ Salakhutdinov · Sergey Levine · Chelsea Finn -
2022 : PnP-Nav: Plug-and-Play Policies for Generalizable Visual Navigation Across Robots »
Dhruv Shah · Ajay Sridhar · Arjun Bhorkar · Noriaki Hirose · Sergey Levine -
2022 : Offline Reinforcement Learning for Customizable Visual Navigation »
Dhruv Shah · Arjun Bhorkar · Hrishit Leen · Ilya Kostrikov · Nicholas Rhinehart · Sergey Levine -
2022 : Giving Robots a Hand: Broadening Generalization via Hand-Centric Human Video Demonstrations »
Moo J Kim · Jiajun Wu · Chelsea Finn -
2022 : Contrastive Value Learning: Implicit Models for Simple Offline RL »
Bogdan Mazoure · Benjamin Eysenbach · Ofir Nachum · Jonathan Tompson -
2022 : Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement »
Michael Chang · Alyssa L Dayan · Franziska Meier · Tom Griffiths · Sergey Levine · Amy Zhang -
2022 : A Connection between One-Step Regularization and Critic Regularization in Reinforcement Learning »
Benjamin Eysenbach · Matthieu Geist · Russ Salakhutdinov · Sergey Levine -
2022 : Simplifying Model-based RL: Learning Representations, Latent-space Models, and Policies with One Objective »
Raj Ghugare · Homanga Bharadhwaj · Benjamin Eysenbach · Sergey Levine · Ruslan Salakhutdinov -
2022 : Learning Successor Feature Representations to Train Robust Policies for Multi-task Learning »
Melissa Mozifian · Dieter Fox · David Meger · Fabio Ramos · Animesh Garg -
2022 : MultiViz: Towards Visualizing and Understanding Multimodal Models »
Paul Pu Liang · · Gunjan Chhablani · Nihal Jain · Zihao Deng · Xingbo Wang · Louis-Philippe Morency · Ruslan Salakhutdinov -
2022 : Nano: Nested Human-in-the-Loop Reward Learning for Controlling Distribution of Generated Text »
Xiang Fan · · Paul Pu Liang · Ruslan Salakhutdinov · Louis-Philippe Morency -
2022 : Surgical Fine-Tuning Improves Adaptation to Distribution Shifts »
Yoonho Lee · Annie Chen · Fahim Tajwar · Ananya Kumar · Huaxiu Yao · Percy Liang · Chelsea Finn -
2022 : Exploring Transformer Backbones for Heterogeneous Treatment Effect Estimation »
yifan zhang · Hanlin Zhang · Zachary Lipton · Li Erran Li · Eric Xing -
2022 : Adversarial Policies Beat Professional-Level Go AIs »
Tony Wang · Adam Gleave · Nora Belrose · Tom Tseng · Michael Dennis · Yawen Duan · Viktor Pogrebniak · Joseph Miller · Sergey Levine · Stuart Russell -
2022 : Train Offline, Test Online: A Real Robot Learning Benchmark »
Gaoyue Zhou · Victoria Dean · Mohan Kumar Srirama · Aravind Rajeswaran · Jyothish Pari · Kyle Hatch · Aryan Jain · Tianhe Yu · Pieter Abbeel · Lerrel Pinto · Chelsea Finn · Abhinav Gupta -
2022 : Interactive Language: Talking to Robots in Real Time »
Corey Lynch · Pete Florence · Jonathan Tompson · Ayzaan Wahid · Tianli Ding · James Betker · Robert Baruch · Travis Armstrong -
2022 : Robotic Skill Acquistion via Instruction Augmentation with Vision-Language Models »
Ted Xiao · Harris Chan · Pierre Sermanet · Ayzaan Wahid · Anthony Brohan · Karol Hausman · Sergey Levine · Jonathan Tompson -
2023 Competition: Train Offline, Test Online: A Democratized Robotics Benchmark »
Victoria Dean · Gaoyue Zhou · Mohan Kumar Srirama · Sudeep Dasari · Esther Brown · Marion Lepert · Paul Ruvolo · Chelsea Finn · Lerrel Pinto · Abhinav Gupta -
2023 Workshop: Workshop on Advancing Neural Network Training (WANT): Computational Efficiency, Scalability, and Resource Optimization »
Julia Gusak · Jean Kossaifi · Alena Shilova · Cristiana Bentes · Animashree Anandkumar · Olivier Beaumont -
2023 Workshop: 6th Robot Learning Workshop: Pretraining, Fine-Tuning, and Generalization with Large Scale Models »
Dhruv Shah · Paula Wulkop · Claas Voelcker · Georgia Chalvatzaki · Alex Bewley · Hamidreza Kasaei · Ransalu Senanayake · Julien PEREZ · Jonathan Tompson -
2023 Workshop: The Symbiosis of Deep Learning and Differential Equations -- III »
Luca Celotti · Martin Magill · Ermal Rrapaj · Winnie Xu · Qiyao Wei · Archis Joglekar · Michael Poli · Animashree Anandkumar -
2023 Workshop: Generalization in Planning (GenPlan '23) »
Pulkit Verma · Siddharth Srivastava · Aviv Tamar · Felipe Trevizan -
2023 Workshop: New Frontiers of AI for Drug Discovery and Development »
Animashree Anandkumar · Ilija Bogunovic · Ti-chiun Chang · Quanquan Gu · Jure Leskovec · Michelle Li · Chong Liu · Nataša Tagasovska · Wei Wang -
2023 Workshop: Workshop on Distribution Shifts: New Frontiers with Foundation Models »
Rebecca Roelofs · Fanny Yang · Hongseok Namkoong · Masashi Sugiyama · Jacob Eisenstein · Pang Wei Koh · Shiori Sagawa · Tatsunori Hashimoto · Yoonho Lee -
2023 Workshop: The NeurIPS 2023 Workshop on Goal-Conditioned Reinforcement Learning »
Benjamin Eysenbach · Ishan Durugkar · Jason Yecheng Ma · Andi Peng · Tongzhou Wang · Amy Zhang -
2023 Competition: The NeurIPS 2023 Neural MMO Challenge: Multi-Task Reinforcement Learning and Curriculum Generation »
Joseph Suarez · Phillip Isola · David Bloomin · Kyoung Choe · Hao Li · Ryan Sullivan · Nishaanth Kanna · Daniel Scott · Rose Shuman · Herbie Bradley · Louis Castricato · Chenghui Yu · Yuhao Jiang · Qimai Li · Jiaxin Chen · Xiaolong Zhu · Dipam Chakrabroty · Sharada Mohanty -
2022 : Sample-Specific Contextualized Graphical Models Using Clinical and Molecular Data Reveal Transcriptional Network Heterogeneity Across 7000 Tumors »
Caleb Ellington · Ben Lengerich · Thomas Watkins · Jiekun Yang · Manolis Kellis · Eric Xing -
2022 : Debate: Robotics for Good »
Karol Hausman · Katherine Driggs-Campbell · Luca Carlone · Sarah Dean · Matthew Johnson-Roberson · Animesh Garg -
2022 : Contributed Talk: DensePure: Understanding Diffusion Models towards Adversarial Robustness »
Zhongzhu Chen · Kun Jin · Jiongxiao Wang · Weili Nie · Mingyan Liu · Anima Anandkumar · Bo Li · Dawn Song -
2022 : Offline Q-learning on Diverse Multi-Task Data Both Scales And Generalizes »
Aviral Kumar · Rishabh Agarwal · XINYANG GENG · George Tucker · Sergey Levine -
2022 : Panel: Scaling & Models (Q&A 2) »
Andy Zeng · Haoran Tang · Karol Hausman · Jackie Kay · Gabriel Barth-Maron -
2022 Workshop: Deep Reinforcement Learning Workshop »
Karol Hausman · Qi Zhang · Matthew Taylor · Martha White · Suraj Nair · Manan Tomar · Risto Vuorio · Ted Xiao · Zeyu Zheng · Manan Tomar -
2022 : Panel: Uncertainty-Aware Machine Learning for Robotics (Q&A 1) »
Georgia Chalvatzaki · Stefanie Tellex · Animesh Garg -
2022 Workshop: 5th Robot Learning Workshop: Trustworthy Robotics »
Alex Bewley · Roberto Calandra · Anca Dragan · Igor Gilitschenski · Emily Hannigan · Masha Itkina · Hamidreza Kasaei · Jens Kober · Danica Kragic · Nathan Lambert · Julien PEREZ · Fabio Ramos · Ransalu Senanayake · Jonathan Tompson · Vincent Vanhoucke · Markus Wulfmeier -
2022 Workshop: Trustworthy and Socially Responsible Machine Learning »
Huan Zhang · Linyi Li · Chaowei Xiao · J. Zico Kolter · Anima Anandkumar · Bo Li -
2022 Workshop: The Symbiosis of Deep Learning and Differential Equations II »
Michael Poli · Winnie Xu · Estefany Kelly Buchanan · Maryam Hosseini · Luca Celotti · Martin Magill · Ermal Rrapaj · Qiyao Wei · Stefano Massaroli · Patrick Kidger · Archis Joglekar · Animesh Garg · David Duvenaud -
2022 Spotlight: Lightning Talks 6A-4 »
Xiu-Shen Wei · Konstantina Dritsa · Guillaume Huguet · ABHRA CHAUDHURI · Zhenbin Wang · Kevin Qinghong Lin · Yutong Chen · Jianan Zhou · Yongsen Mao · Junwei Liang · Jinpeng Wang · Mao Ye · Yiming Zhang · Aikaterini Thoma · H.-Y. Xu · Daniel Sumner Magruder · Enwei Zhang · Jianing Zhu · Ronglai Zuo · Massimiliano Mancini · Hanxiao Jiang · Jun Zhang · Fangyun Wei · Faen Zhang · Ioannis Pavlopoulos · Zeynep Akata · Xiatian Zhu · Jingfeng ZHANG · Alexander Tong · Mattia Soldan · Chunhua Shen · Yuxin Peng · Liuhan Peng · Michael Wray · Tongliang Liu · Anjan Dutta · Yu Wu · Oluwadamilola Fasina · Panos Louridas · Angel Chang · Manik Kuchroo · Manolis Savva · Shujie LIU · Wei Zhou · Rui Yan · Gang Niu · Liang Tian · Bo Han · Eric Z. XU · Guy Wolf · Yingying Zhu · Brian Mak · Difei Gao · Masashi Sugiyama · Smita Krishnaswamy · Rong-Cheng Tu · Wenzhe Zhao · Weijie Kong · Chengfei Cai · WANG HongFa · Dima Damen · Bernard Ghanem · Wei Liu · Mike Zheng Shou -
2022 Spotlight: Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks »
Jianan Zhou · Jianing Zhu · Jingfeng ZHANG · Tongliang Liu · Gang Niu · Bo Han · Masashi Sugiyama -
2022 : Link-level Track: Intro »
Hongyu Ren -
2022 Competition: OGB-LSC 2022: A Large-Scale Challenge for ML on Graphs »
Weihua Hu · Matthias Fey · Hongyu Ren · Maho Nakata · Yuxiao Dong · Jure Leskovec -
2022 Competition: The Third Neural MMO Challenge: Learning to Specialize in Massively Multiagent Open Worlds »
Joseph Suarez · Hanmo Chen · Arbin Chen · Bo Wu · Xiaolong Zhu · enhong liu · JUN HU · Chenghui Yu · Phillip Isola -
2022 Spotlight: Lightning Talks 3B-3 »
Sitao Luan · Zhiyuan You · Ruofan Liu · Linhao Qu · Yuwei Fu · Jiaxi Wang · Chunyu Wei · Jian Liang · xiaoyuan luo · Di Wu · Yun Lin · Lei Cui · Ji Wu · Chenqing Hua · Yujun Shen · Qincheng Lu · XIANGLIN YANG · Benoit Boulet · Manning Wang · Di Liu · Lei Huang · Fei Wang · Kai Yang · Jiaqi Zhu · Jin Song Dong · Zhijian Song · Xin Lu · Mingde Zhao · Shuyuan Zhang · Yu Zheng · Xiao-Wen Chang · Xinyi Le · Doina Precup -
2022 Spotlight: Revisiting Heterophily For Graph Neural Networks »
Sitao Luan · Chenqing Hua · Qincheng Lu · Jiaqi Zhu · Mingde Zhao · Shuyuan Zhang · Xiao-Wen Chang · Doina Precup -
2022 : Simulating Human Gaze with Neural Visual Attention »
Leo Schwinn · Doina Precup · Bjoern Eskofier · Dario Zanca -
2022 : Train Offline, Test Online: A Real Robot Learning Benchmark »
Gaoyue Zhou · Victoria Dean · Mohan Kumar Srirama · Aravind Rajeswaran · Jyothish Pari · Kyle Hatch · Aryan Jain · Tianhe Yu · Pieter Abbeel · Lerrel Pinto · Chelsea Finn · Abhinav Gupta -
2022 : HEAT: Hardware-Efficient Automatic Tensor Decomposition for Transformer Compression »
Jiaqi Gu · Ben Keller · Jean Kossaifi · Anima Anandkumar · Brucek Khailany · David Pan -
2022 : Train Offline, Test Online: A Real Robot Learning Benchmark »
Gaoyue Zhou · Victoria Dean · Mohan Kumar Srirama · Aravind Rajeswaran · Jyothish Pari · Kyle Hatch · Aryan Jain · Tianhe Yu · Pieter Abbeel · Lerrel Pinto · Chelsea Finn · Abhinav Gupta -
2022 : Simulating Human Gaze with Neural Visual Attention »
Leo Schwinn · Doina Precup · Bjoern Eskofier · Dario Zanca -
2022 : Dynamic-backbone protein-ligand structure prediction with multiscale generative diffusion models »
Zhuoran Qiao · Weili Nie · Arash Vahdat · Thomas Miller · Anima Anandkumar -
2022 Workshop: Workshop on Distribution Shifts: Connecting Methods and Applications »
Chelsea Finn · Fanny Yang · Hongseok Namkoong · Masashi Sugiyama · Jacob Eisenstein · Jonas Peters · Rebecca Roelofs · Shiori Sagawa · Pang Wei Koh · Yoonho Lee -
2022 Workshop: Machine Learning and the Physical Sciences »
Atilim Gunes Baydin · Adji Bousso Dieng · Emine Kucukbenli · Gilles Louppe · Siddharth Mishra-Sharma · Benjamin Nachman · Brian Nord · Savannah Thais · Anima Anandkumar · Kyle Cranmer · Lenka Zdeborová · Rianne van den Berg -
2022 : Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement »
Michael Chang · Alyssa L Dayan · Franziska Meier · Tom Griffiths · Sergey Levine · Amy Zhang -
2022 : Line Drawings as Communication »
Phillip Isola -
2022 Workshop: All Things Attention: Bridging Different Perspectives on Attention »
Abhijat Biswas · Akanksha Saran · Khimya Khetarpal · Reuben Aronson · Ruohan Zhang · Grace Lindsay · Scott Niekum -
2022 Workshop: 3rd Offline Reinforcement Learning Workshop: Offline RL as a "Launchpad" »
Aviral Kumar · Rishabh Agarwal · Aravind Rajeswaran · Wenxuan Zhou · George Tucker · Doina Precup · Aviral Kumar -
2022 Workshop: AI for Science: Progress and Promises »
Yi Ding · Yuanqi Du · Tianfan Fu · Hanchen Wang · Anima Anandkumar · Yoshua Bengio · Anthony Gitter · Carla Gomes · Aviv Regev · Max Welling · Marinka Zitnik -
2022 Poster: Adaptive Interest for Emphatic Reinforcement Learning »
Martin Klissarov · Rasool Fakoor · Jonas Mueller · Kavosh Asadi · Taesup Kim · Alexander Smola -
2022 Poster: On the Effectiveness of Fine-tuning Versus Meta-reinforcement Learning »
Mandi Zhao · Pieter Abbeel · Stephen James -
2022 Poster: Semantic uncertainty intervals for disentangled latent spaces »
Swami Sankaranarayanan · Anastasios Angelopoulos · Stephen Bates · Yaniv Romano · Phillip Isola -
2022 Poster: MEMO: Test Time Robustness via Adaptation and Augmentation »
Marvin Zhang · Sergey Levine · Chelsea Finn -
2022 Poster: Adapting to Online Label Shift with Provable Guarantees »
Yong Bai · Yu-Jie Zhang · Peng Zhao · Masashi Sugiyama · Zhi-Hua Zhou -
2022 Poster: Zero-shot Transfer Learning within a Heterogeneous Graph via Knowledge Transfer Networks »
Minji Yoon · John Palowitch · Dustin Zelle · Ziniu Hu · Ruslan Salakhutdinov · Bryan Perozzi -
2022 Poster: Learning Options via Compression »
Yiding Jiang · Evan Liu · Benjamin Eysenbach · J. Zico Kolter · Chelsea Finn -
2022 Poster: Chain of Thought Imitation with Procedure Cloning »
Mengjiao (Sherry) Yang · Dale Schuurmans · Pieter Abbeel · Ofir Nachum -
2022 Poster: First Contact: Unsupervised Human-Machine Co-Adaptation via Mutual Information Maximization »
Siddharth Reddy · Sergey Levine · Anca Dragan -
2022 Poster: Inductive Logical Query Answering in Knowledge Graphs »
Michael Galkin · Zhaocheng Zhu · Hongyu Ren · Jian Tang -
2022 Poster: Revisiting Heterophily For Graph Neural Networks »
Sitao Luan · Chenqing Hua · Qincheng Lu · Jiaqi Zhu · Mingde Zhao · Shuyuan Zhang · Xiao-Wen Chang · Doina Precup -
2022 Poster: Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models »
Manli Shu · Weili Nie · De-An Huang · Zhiding Yu · Tom Goldstein · Anima Anandkumar · Chaowei Xiao -
2022 Poster: DASCO: Dual-Generator Adversarial Support Constrained Offline Reinforcement Learning »
Quan Vuong · Aviral Kumar · Sergey Levine · Yevgen Chebotar -
2022 Poster: Emergent Communication: Generalization and Overfitting in Lewis Games »
Mathieu Rita · Corentin Tallec · Paul Michel · Jean-Bastien Grill · Olivier Pietquin · Emmanuel Dupoux · Florian Strub -
2022 Poster: PeRFception: Perception using Radiance Fields »
Yoonwoo Jeong · Seungjoo Shin · Junha Lee · Chris Choy · Anima Anandkumar · Minsu Cho · Jaesik Park -
2022 Poster: In Defense of the Unitary Scalarization for Deep Multi-Task Learning »
Vitaly Kurin · Alessandro De Palma · Ilya Kostrikov · Shimon Whiteson · Pawan K Mudigonda -
2022 Poster: Meta Reinforcement Learning with Finite Training Tasks - a Density Estimation Approach »
Zohar Rimon · Aviv Tamar · Gilad Adler -
2022 Poster: Masked Autoencoding for Scalable and Generalizable Decision Making »
Fangchen Liu · Hao Liu · Aditya Grover · Pieter Abbeel -
2022 Poster: Deep Bidirectional Language-Knowledge Graph Pretraining »
Michihiro Yasunaga · Antoine Bosselut · Hongyu Ren · Xikun Zhang · Christopher D Manning · Percy Liang · Jure Leskovec -
2022 Poster: Adversarial Unlearning: Reducing Confidence Along Adversarial Directions »
Amrith Setlur · Benjamin Eysenbach · Virginia Smith · Sergey Levine -
2022 Poster: Mismatched No More: Joint Model-Policy Optimization for Model-Based RL »
Benjamin Eysenbach · Alexander Khazatsky · Sergey Levine · Russ Salakhutdinov -
2022 Poster: Procedural Image Programs for Representation Learning »
Manel Baradad · Richard Chen · Jonas Wulff · Tongzhou Wang · Rogerio Feris · Antonio Torralba · Phillip Isola -
2022 Poster: Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexity »
Abhishek Gupta · Aldo Pacchiano · Yuexiang Zhai · Sham Kakade · Sergey Levine -
2022 Poster: Few-shot Relational Reasoning via Connection Subgraph Pretraining »
Qian Huang · Hongyu Ren · Jure Leskovec -
2022 Poster: Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits »
Tianyuan Jin · Pan Xu · Xiaokui Xiao · Anima Anandkumar -
2022 Poster: Distributionally Adaptive Meta Reinforcement Learning »
Anurag Ajay · Abhishek Gupta · Dibya Ghosh · Sergey Levine · Pulkit Agrawal -
2022 Poster: You Only Live Once: Single-Life Reinforcement Learning »
Annie Chen · Archit Sharma · Sergey Levine · Chelsea Finn -
2022 Poster: Test-Time Training with Masked Autoencoders »
Yossi Gandelsman · Yu Sun · Xinlei Chen · Alexei Efros -
2022 Poster: Visual Prompting via Image Inpainting »
Amir Bar · Yossi Gandelsman · Trevor Darrell · Amir Globerson · Alexei Efros -
2022 Poster: Learning Chaotic Dynamics in Dissipative Systems »
Zongyi Li · Miguel Liu-Schiaffini · Nikola Kovachki · Kamyar Azizzadenesheli · Burigede Liu · Kaushik Bhattacharya · Andrew Stuart · Anima Anandkumar -
2022 Poster: Generating Long Videos of Dynamic Scenes »
Tim Brooks · Janne Hellsten · Miika Aittala · Ting-Chun Wang · Timo Aila · Jaakko Lehtinen · Ming-Yu Liu · Alexei Efros · Tero Karras -
2022 Poster: Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time »
Huaxiu Yao · Caroline Choi · Bochuan Cao · Yoonho Lee · Pang Wei Koh · Chelsea Finn -
2022 Poster: Truncated Emphatic Temporal Difference Methods for Prediction and Control »
Shangtong Zhang · Shimon Whiteson -
2022 Poster: Fast and Robust Rank Aggregation against Model Misspecification »
YUANGANG PAN · Ivor W. Tsang · Weijie Chen · Gang Niu · Masashi Sugiyama -
2022 Poster: Unsupervised Reinforcement Learning with Contrastive Intrinsic Control »
Michael Laskin · Hao Liu · Xue Bin Peng · Denis Yarats · Aravind Rajeswaran · Pieter Abbeel -
2022 Poster: Object Representations as Fixed Points: Training Iterative Refinement Algorithms with Implicit Differentiation »
Michael Chang · Tom Griffiths · Sergey Levine -
2022 Poster: When to Ask for Help: Proactive Interventions in Autonomous Reinforcement Learning »
Annie Xie · Fahim Tajwar · Archit Sharma · Chelsea Finn -
2022 Poster: Data-Driven Offline Decision-Making via Invariant Representation Learning »
Han Qi · Yi Su · Aviral Kumar · Sergey Levine -
2022 Poster: Exploring the Limits of Domain-Adaptive Training for Detoxifying Large-Scale Language Models »
Boxin Wang · Wei Ping · Chaowei Xiao · Peng Xu · Mostofa Patwary · Mohammad Shoeybi · Bo Li · Anima Anandkumar · Bryan Catanzaro -
2022 Poster: Offline Multi-Agent Reinforcement Learning with Knowledge Distillation »
Wei-Cheng Tseng · Tsun-Hsuan Johnson Wang · Yen-Chen Lin · Phillip Isola -
2022 Poster: Pre-Trained Language Models for Interactive Decision-Making »
Shuang Li · Xavier Puig · Chris Paxton · Yilun Du · Clinton Wang · Linxi Fan · Tao Chen · De-An Huang · Ekin Akyürek · Anima Anandkumar · Jacob Andreas · Igor Mordatch · Antonio Torralba · Yuke Zhu -
2022 Poster: Contrastive Learning as Goal-Conditioned Reinforcement Learning »
Benjamin Eysenbach · Tianjun Zhang · Sergey Levine · Russ Salakhutdinov -
2022 Poster: Spending Thinking Time Wisely: Accelerating MCTS with Virtual Expansions »
Weirui Ye · Pieter Abbeel · Yang Gao -
2022 Poster: Deep Hierarchical Planning from Pixels »
Danijar Hafner · Kuang-Huei Lee · Ian Fischer · Pieter Abbeel -
2022 Poster: C-Mixup: Improving Generalization in Regression »
Huaxiu Yao · Yiping Wang · Linjun Zhang · James Zou · Chelsea Finn -
2022 Poster: Synergy-of-Experts: Collaborate to Improve Adversarial Robustness »
Sen Cui · Jingfeng ZHANG · Jian Liang · Bo Han · Masashi Sugiyama · Changshui Zhang -
2022 Poster: MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge »
Linxi Fan · Guanzhi Wang · Yunfan Jiang · Ajay Mandlekar · Yuncong Yang · Haoyi Zhu · Andrew Tang · De-An Huang · Yuke Zhu · Anima Anandkumar -
2022 Poster: Distributed Influence-Augmented Local Simulators for Parallel MARL in Large Networked Systems »
Miguel Suau · Jinke He · Mustafa Mert Çelikok · Matthijs Spaan · Frans Oliehoek -
2022 Poster: Continuous MDP Homomorphisms and Homomorphic Policy Gradient »
Sahand Rezaei-Shoshtari · Rosie Zhao · Prakash Panangaden · David Meger · Doina Precup -
2022 Poster: Giving Feedback on Interactive Student Programs with Meta-Exploration »
Evan Liu · Moritz Stephan · Allen Nie · Chris Piech · Emma Brunskill · Chelsea Finn -
2022 Poster: Learning Contrastive Embedding in Low-Dimensional Space »
Shuo Chen · Chen Gong · Jun Li · Jian Yang · Gang Niu · Masashi Sugiyama -
2022 Poster: Imitating Past Successes can be Very Suboptimal »
Benjamin Eysenbach · Soumith Udatha · Russ Salakhutdinov · Sergey Levine -
2022 Poster: Improving Zero-Shot Generalization in Offline Reinforcement Learning using Generalized Similarity Functions »
Bogdan Mazoure · Ilya Kostrikov · Ofir Nachum · Jonathan Tompson -
2022 Poster: Equivariant Networks for Zero-Shot Coordination »
Darius Muglich · Christian Schroeder de Witt · Elise van der Pol · Shimon Whiteson · Jakob Foerster -
2022 Poster: Myriad: a real-world testbed to bridge trajectory optimization and deep learning »
Nikolaus Howe · Simon Dufort-Labbé · Nitarshan Rajkumar · Pierre-Luc Bacon -
2021 : Lifelong Robotic Reinforcement Learning by Retaining Experiences »
Annie Xie · Chelsea Finn -
2021 : Anima Anandkumar »
Anima Anandkumar -
2021 : Retrospective Panel »
Sergey Levine · Nando de Freitas · Emma Brunskill · Finale Doshi-Velez · Nan Jiang · Rishabh Agarwal -
2021 : Panel B: Safe Learning and Decision Making in Uncertain and Unstructured Environments »
Yisong Yue · J. Zico Kolter · Ivan Dario D Jimenez Rodriguez · Dragos Margineantu · Animesh Garg · Melissa Greeff -
2021 : Reinforcement Learning in Factored Action Spaces using Tensor Decompositions »
Anuj Mahajan · Mikayel Samvelyan · Lei Mao · Viktor Makoviichuk · Animesh Garg · Jean Kossaifi · Shimon Whiteson · Yuke Zhu · Anima Anandkumar -
2021 : Model based multi-agent reinforcement learning with tensor decompositions »
Pascal van der Vaart · Anuj Mahajan · Shimon Whiteson -
2021 : Theme B Introduction »
Animesh Garg -
2021 : Playful Interactions for Representation Learning »
Sarah Young · Pieter Abbeel · Lerrel Pinto -
2021 : General Discussion 2 - What does the OOD problem mean to you and your field? with Anima Anandkumar, Terry Sejnowski, Chris White: General Discussion 2 »
Anima Anandkumar · Terry Sejnowski · Weiwei Yang · Joshua T Vogelstein -
2021 : Anima Anandkumar: Role of AI in predicting and mitigating climate change »
Anima Anandkumar -
2021 Workshop: Offline Reinforcement Learning »
Rishabh Agarwal · Aviral Kumar · George Tucker · Justin Fu · Nan Jiang · Doina Precup · Aviral Kumar -
2021 Workshop: Math AI for Education (MATHAI4ED): Bridging the Gap Between Research and Smart Education »
Pan Lu · Yuhuai Wu · Sean Welleck · Xiaodan Liang · Eric Xing · James McClelland -
2021 Workshop: Deployable Decision Making in Embodied Systems (DDM) »
Angela Schoellig · Animesh Garg · Somil Bansal · SiQi Zhou · Melissa Greeff · Lukas Brunke -
2021 : Efficient Quantum Optimization via Multi-Basis Encodings and Tensor Rings »
Anima Anandkumar -
2021 Workshop: Second Workshop on Quantum Tensor Networks in Machine Learning »
Xiao-Yang Liu · Qibin Zhao · Ivan Oseledets · Yufei Ding · Guillaume Rabusseau · Jean Kossaifi · Khadijeh Najafi · Anwar Walid · Andrzej Cichocki · Masashi Sugiyama -
2021 Workshop: Ecological Theory of Reinforcement Learning: How Does Task Design Influence Agent Learning? »
Manfred Díaz · Hiroki Furuta · Elise van der Pol · Lisa Lee · Shixiang (Shane) Gu · Pablo Samuel Castro · Simon Du · Marc Bellemare · Sergey Levine -
2021 Workshop: The Symbiosis of Deep Learning and Differential Equations »
Luca Celotti · Kelly Buchanan · Jorge Ortiz · Patrick Kidger · Stefano Massaroli · Michael Poli · Lily Hu · Ermal Rrapaj · Martin Magill · Thorsteinn Jonsson · Animesh Garg · Murtadha Aldeer -
2021 : Discussion: Chelsea Finn, Masashi Sugiyama »
Chelsea Finn · Masashi Sugiyama -
2021 : Robustness through the Lens of Invariance »
Chelsea Finn -
2021 : Karol Hausman Talk Q&A »
Karol Hausman -
2021 : Importance Weighting for Transfer Learning »
Masashi Sugiyama -
2021 : Invited Talk: Karol Hausman - Reinforcement Learning as a Data Sponge »
Karol Hausman -
2021 : Data-Driven Offline Optimization for Architecting Hardware Accelerators »
Aviral Kumar · Amir Yazdanbakhsh · Milad Hashemi · Kevin Swersky · Sergey Levine -
2021 : Low-Precision Training in Logarithmic Number System using Multiplicative Weight Update »
Jiawei Zhao · Steve Dai · Rangha Venkatesan · Brian Zimmer · Mustafa Ali · Ming-Yu Liu · Brucek Khailany · · Anima Anandkumar -
2021 : Sergey Levine Talk Q&A »
Sergey Levine -
2021 : Safe RL Debate »
Sylvia Herbert · Animesh Garg · Emma Brunskill · Aleksandra Faust · Dylan Hadfield-Menell -
2021 : Opinion Contributed Talk: Sergey Levine »
Sergey Levine -
2021 : Offline Meta-Reinforcement Learning with Online Self-Supervision Q&A »
Vitchyr Pong · Ashvin Nair · Laura Smith · Catherine Huang · Sergey Levine -
2021 : Offline Meta-Reinforcement Learning with Online Self-Supervision »
Vitchyr Pong · Ashvin Nair · Laura Smith · Catherine Huang · Sergey Levine -
2021 : Offline Meta-Reinforcement Learning with Online Self-Supervision »
Vitchyr Pong · Ashvin Nair · Laura Smith · Catherine Huang · Sergey Levine -
2021 : Accelerating Systems and ML for Science »
Anima Anandkumar -
2021 : Safe RL Panel Discussion »
Animesh Garg · Marek Petrik · Shie Mannor · Claire Tomlin · Ugo Rosolia · Dylan Hadfield-Menell -
2021 : DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization Q&A »
Aviral Kumar · Rishabh Agarwal · Tengyu Ma · Aaron Courville · George Tucker · Sergey Levine -
2021 : DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization »
Aviral Kumar · Rishabh Agarwal · Tengyu Ma · Aaron Courville · George Tucker · Sergey Levine -
2021 : Implicit Behavioral Cloning Q&A »
Pete Florence · Corey Lynch · Andy Zeng · Oscar Ramirez · Ayzaan Wahid · Laura Downs · Adrian Wong · Igor Mordatch · Jonathan Tompson -
2021 Workshop: Distribution shifts: connecting methods and applications (DistShift) »
Shiori Sagawa · Pang Wei Koh · Fanny Yang · Hongseok Namkoong · Jiashi Feng · Kate Saenko · Percy Liang · Sarah Bird · Sergey Levine -
2021 : Implicit Behavioral Cloning »
Pete Florence · Corey Lynch · Andy Zeng · Oscar Ramirez · Ayzaan Wahid · Laura Downs · Adrian Wong · Igor Mordatch · Jonathan Tompson -
2021 Workshop: Deep Reinforcement Learning »
Pieter Abbeel · Chelsea Finn · David Silver · Matthew Taylor · Martha White · Srijita Das · Yuqing Du · Andrew Patterson · Manan Tomar · Olivia Watkins -
2021 Workshop: Machine Learning and the Physical Sciences »
Anima Anandkumar · Kyle Cranmer · Mr. Prabhat · Lenka Zdeborová · Atilim Gunes Baydin · Juan Carrasquilla · Emine Kucukbenli · Gilles Louppe · Benjamin Nachman · Brian Nord · Savannah Thais -
2021 Oral: Replacing Rewards with Examples: Example-Based Policy Search via Recursive Classification »
Ben Eysenbach · Sergey Levine · Russ Salakhutdinov -
2021 Poster: Visual Adversarial Imitation Learning using Variational Models »
Rafael Rafailov · Tianhe Yu · Aravind Rajeswaran · Chelsea Finn -
2021 Poster: Hindsight Task Relabelling: Experience Replay for Sparse Reward Meta-RL »
Charles Packer · Pieter Abbeel · Joseph Gonzalez -
2021 Poster: Learning to Ground Multi-Agent Communication with Autoencoders »
Toru Lin · Jacob Huh · Christopher Stauffer · Ser Nam Lim · Phillip Isola -
2021 Poster: Improving Computational Efficiency in Visual Reinforcement Learning via Stored Embeddings »
Lili Chen · Kimin Lee · Aravind Srinivas · Pieter Abbeel -
2021 : The NeurIPS 2021 BEETL Competition: Benchmarks for EEG Transfer Learning + Q&A »
Xiaoxi Wei · Vinay Jayaram · Sylvain Chevallier · Giulia Luise · Camille Jeunet · Moritz Grosse-Wentrup · Alexandre Gramfort · Aldo A Faisal -
2021 Poster: Combiner: Full Attention Transformer with Sparse Computation Cost »
Hongyu Ren · Hanjun Dai · Zihang Dai · Mengjiao (Sherry) Yang · Jure Leskovec · Dale Schuurmans · Bo Dai -
2021 Poster: Robust Predictable Control »
Ben Eysenbach · Russ Salakhutdinov · Sergey Levine -
2021 Poster: ProTo: Program-Guided Transformer for Program-Guided Tasks »
Zelin Zhao · Karan Samel · Binghong Chen · lee song -
2021 Poster: Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones »
Yushi Bai · Zhitao Ying · Hongyu Ren · Jure Leskovec -
2021 Poster: Understanding and Improving Early Stopping for Learning with Noisy Labels »
Yingbin Bai · Erkun Yang · Bo Han · Yanhua Yang · Jiatong Li · Yinian Mao · Gang Niu · Tongliang Liu -
2021 Poster: Efficiently Identifying Task Groupings for Multi-Task Learning »
Chris Fifty · Ehsan Amid · Zhe Zhao · Tianhe Yu · Rohan Anil · Chelsea Finn -
2021 Poster: Which Mutual-Information Representation Learning Objectives are Sufficient for Control? »
Kate Rakelly · Abhishek Gupta · Carlos Florensa · Sergey Levine -
2021 Poster: COMBO: Conservative Offline Model-Based Policy Optimization »
Tianhe Yu · Aviral Kumar · Rafael Rafailov · Aravind Rajeswaran · Sergey Levine · Chelsea Finn -
2021 : BASALT: A MineRL Competition on Solving Human-Judged Task + Q&A »
Rohin Shah · Cody Wild · Steven Wang · Neel Alex · Brandon Houghton · William Guss · Sharada Mohanty · Stephanie Milani · Nicholay Topin · Pieter Abbeel · Stuart Russell · Anca Dragan -
2021 Poster: FACMAC: Factored Multi-Agent Centralised Policy Gradients »
Bei Peng · Tabish Rashid · Christian Schroeder de Witt · Pierre-Alexandre Kamienny · Philip Torr · Wendelin Boehmer · Shimon Whiteson -
2021 Poster: Drop-DTW: Aligning Common Signal Between Sequences While Dropping Outliers »
Mikita Dvornik · Isma Hadji · Konstantinos Derpanis · Animesh Garg · Allan Jepson -
2021 Poster: Learning to See by Looking at Noise »
Manel Baradad Jurjo · Jonas Wulff · Tongzhou Wang · Phillip Isola · Antonio Torralba -
2021 Poster: Outcome-Driven Reinforcement Learning via Variational Inference »
Tim G. J. Rudner · Vitchyr Pong · Rowan McAllister · Yarin Gal · Sergey Levine -
2021 Poster: Bayesian Bellman Operators »
Mattie Fellows · Kristian Hartikainen · Shimon Whiteson -
2021 Poster: Decision Transformer: Reinforcement Learning via Sequence Modeling »
Lili Chen · Kevin Lu · Aravind Rajeswaran · Kimin Lee · Aditya Grover · Misha Laskin · Pieter Abbeel · Aravind Srinivas · Igor Mordatch -
2021 Poster: Offline Meta Reinforcement Learning -- Identifiability Challenges and Effective Data Collection Strategies »
Ron Dorfman · Idan Shenfeld · Aviv Tamar -
2021 Poster: There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning »
Nathan Grinsztajn · Johan Ferret · Olivier Pietquin · philippe preux · Matthieu Geist -
2021 Poster: Bayesian Adaptation for Covariate Shift »
Aurick Zhou · Sergey Levine -
2021 Poster: Multi-task Learning of Order-Consistent Causal Graphs »
Xinshi Chen · Haoran Sun · Caleb Ellington · Eric Xing · Le Song -
2021 Poster: Adaptable Agent Populations via a Generative Model of Policies »
Kenneth Derek · Phillip Isola -
2021 Poster: Flexible Option Learning »
Martin Klissarov · Doina Precup -
2021 Poster: Offline Reinforcement Learning as One Big Sequence Modeling Problem »
Michael Janner · Qiyang Li · Sergey Levine -
2021 Poster: Pragmatic Image Compression for Human-in-the-Loop Decision-Making »
Sid Reddy · Anca Dragan · Sergey Levine -
2021 Poster: Replacing Rewards with Examples: Example-Based Policy Search via Recursive Classification »
Ben Eysenbach · Sergey Levine · Russ Salakhutdinov -
2021 Poster: Loss function based second-order Jensen inequality and its application to particle variational inference »
Futoshi Futami · Tomoharu Iwata · naonori ueda · Issei Sato · Masashi Sugiyama -
2021 Poster: Regularized Softmax Deep Multi-Agent Q-Learning »
Ling Pan · Tabish Rashid · Bei Peng · Longbo Huang · Shimon Whiteson -
2021 Poster: Mastering Atari Games with Limited Data »
Weirui Ye · Shaohuai Liu · Thanard Kurutach · Pieter Abbeel · Yang Gao -
2021 Poster: Probabilistic Margins for Instance Reweighting in Adversarial Training »
qizhou wang · Feng Liu · Bo Han · Tongliang Liu · Chen Gong · Gang Niu · Mingyuan Zhou · Masashi Sugiyama -
2021 Poster: What Matters for Adversarial Imitation Learning? »
Manu Orsini · Anton Raichuk · Leonard Hussenot · Damien Vincent · Robert Dadashi · Sertan Girgin · Matthieu Geist · Olivier Bachem · Olivier Pietquin · Marcin Andrychowicz -
2021 Poster: Information is Power: Intrinsic Control via Information Capture »
Nicholas Rhinehart · Jenny Wang · Glen Berseth · John Co-Reyes · Danijar Hafner · Chelsea Finn · Sergey Levine -
2021 Poster: Conservative Data Sharing for Multi-Task Offline Reinforcement Learning »
Tianhe Yu · Aviral Kumar · Yevgen Chebotar · Karol Hausman · Sergey Levine · Chelsea Finn -
2021 Poster: Meta-learning with an Adaptive Task Scheduler »
Huaxiu Yao · Yu Wang · Ying Wei · Peilin Zhao · Mehrdad Mahdavi · Defu Lian · Chelsea Finn -
2021 Poster: Reinforcement Learning with Latent Flow »
Wenling Shang · Xiaofei Wang · Aravind Srinivas · Aravind Rajeswaran · Yang Gao · Pieter Abbeel · Misha Laskin -
2021 : Diamond: A MineRL Competition on Training Sample-Efficient Agents + Q&A »
William Guss · Alara Dirik · Byron Galbraith · Brandon Houghton · Anssi Kanervisto · Noboru Kuno · Stephanie Milani · Sharada Mohanty · Karolis Ramanauskas · Ruslan Salakhutdinov · Rohin Shah · Nicholay Topin · Steven Wang · Cody Wild -
2021 Poster: Noether Networks: meta-learning useful conserved quantities »
Ferran Alet · Dylan Doblar · Allan Zhou · Josh Tenenbaum · Kenji Kawaguchi · Chelsea Finn -
2021 Poster: Temporally Abstract Partial Models »
Khimya Khetarpal · Zafarali Ahmed · Gheorghe Comanici · Doina Precup -
2021 Poster: Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability »
Dibya Ghosh · Jad Rahme · Aviral Kumar · Amy Zhang · Ryan Adams · Sergey Levine -
2021 Poster: Snowflake: Scaling GNNs to high-dimensional continuous control via parameter freezing »
Charles Blake · Vitaly Kurin · Maximilian Igl · Shimon Whiteson -
2021 Poster: Behavior From the Void: Unsupervised Active Pre-Training »
Hao Liu · Pieter Abbeel -
2021 Poster: Neural Hybrid Automata: Learning Dynamics With Multiple Modes and Stochastic Transitions »
Michael Poli · Stefano Massaroli · Luca Scimeca · Sanghyuk Chun · Seong Joon Oh · Atsushi Yamashita · Hajime Asama · Jinkyoo Park · Animesh Garg -
2021 Poster: Instance-dependent Label-noise Learning under a Structural Causal Model »
Yu Yao · Tongliang Liu · Mingming Gong · Bo Han · Gang Niu · Kun Zhang -
2021 Poster: Teachable Reinforcement Learning via Advice Distillation »
Olivia Watkins · Abhishek Gupta · Trevor Darrell · Pieter Abbeel · Jacob Andreas -
2021 Poster: MarioNette: Self-Supervised Sprite Learning »
Dmitriy Smirnov · MICHAEL GHARBI · Matthew Fisher · Vitor Guizilini · Alexei Efros · Justin Solomon -
2021 Poster: Differentiable Annealed Importance Sampling and the Perils of Gradient Noise »
Guodong Zhang · Kyle Hsu · Jianing Li · Chelsea Finn · Roger Grosse -
2021 Poster: Scallop: From Probabilistic Deductive Databases to Scalable Differentiable Reasoning »
Jiani Huang · Ziyang Li · Binghong Chen · Karan Samel · Mayur Naik · Le Song · Xujie Si -
2021 Poster: Autonomous Reinforcement Learning via Subgoal Curricula »
Archit Sharma · Abhishek Gupta · Sergey Levine · Karol Hausman · Chelsea Finn -
2021 Poster: Dynamic Bottleneck for Robust Self-Supervised Exploration »
Chenjia Bai · Lingxiao Wang · Lei Han · Animesh Garg · Jianye Hao · Peng Liu · Zhaoran Wang -
2021 Poster: Adaptive Risk Minimization: Learning to Adapt to Domain Shift »
Marvin Zhang · Henrik Marklund · Nikita Dhawan · Abhishek Gupta · Sergey Levine · Chelsea Finn -
2020 : Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization »
Brandon Trabucco · Aviral Kumar · XINYANG GENG · Sergey Levine -
2020 : Conservative Objective Models: A Simple Approach to Effective Model-Based Optimization »
Brandon Trabucco · Aviral Kumar · XINYANG GENG · Sergey Levine -
2020 : Panel Discussion & Closing »
Yejin Choi · Alexei Efros · Chelsea Finn · Kristen Grauman · Quoc V Le · Yann LeCun · Ruslan Salakhutdinov · Eric Xing -
2020 : QA: Chelsea Finn »
Chelsea Finn -
2020 : Mini-panel discussion 3 - Prioritizing Real World RL Challenges »
Chelsea Finn · Thomas Dietterich · Angela Schoellig · Anca Dragan · Anusha Nagabandi · Doina Precup -
2020 : Invited Talk: Chelsea Finn »
Chelsea Finn -
2020 : Keynote: Chelsea Finn »
Chelsea Finn -
2020 : Closing remarks »
Raymond Chua · Feryal Behbahani · Julie J Lee · Rui Ponte Costa · Doina Precup · Blake Richards · Ida Momennejad -
2020 : Invited Talk #7 QnA - Yael Niv »
Yael Niv · Doina Precup · Raymond Chua · Feryal Behbahani -
2020 : Speaker Introduction: Yael Niv »
Doina Precup · Raymond Chua · Feryal Behbahani -
2020 : QA: Alexei Efros »
Alexei Efros -
2020 : Invited Talk: Alexei Efros »
Alexei Efros -
2020 : Panel »
Emma Brunskill · Nan Jiang · Nando de Freitas · Finale Doshi-Velez · Sergey Levine · John Langford · Lihong Li · George Tucker · Rishabh Agarwal · Aviral Kumar -
2020 : Mini-panel discussion 1 - Bridging the gap between theory and practice »
Aviv Tamar · Emma Brunskill · Jost Tobias Springenberg · Omer Gottesman · Daniel Mankowitz -
2020 : QA: Ruslan Salakhutdinov »
Ruslan Salakhutdinov -
2020 : Invited Talk: Ruslan Salakhutdinov »
Ruslan Salakhutdinov -
2020 Workshop: Offline Reinforcement Learning »
Aviral Kumar · Rishabh Agarwal · George Tucker · Lihong Li · Doina Precup · Aviral Kumar -
2020 Workshop: Self-Supervised Learning -- Theory and Practice »
Pengtao Xie · Shanghang Zhang · Pulkit Agrawal · Ishan Misra · Cynthia Rudin · Abdelrahman Mohamed · Wenzhen Yuan · Barret Zoph · Laurens van der Maaten · Xingyi Yang · Eric Xing -
2020 : Panel Discussions »
Grace Lindsay · George Konidaris · Shakir Mohamed · Kimberly Stachenfeld · Peter Dayan · Yael Niv · Doina Precup · Catherine Hartley · Ishita Dasgupta -
2020 : Keynote: Aviv Tamar »
Aviv Tamar -
2020 Workshop: Biological and Artificial Reinforcement Learning »
Raymond Chua · Feryal Behbahani · Julie J Lee · Sara Zannone · Rui Ponte Costa · Blake Richards · Ida Momennejad · Doina Precup -
2020 : Organizers Opening Remarks »
Raymond Chua · Feryal Behbahani · Julie J Lee · Ida Momennejad · Rui Ponte Costa · Blake Richards · Doina Precup -
2020 : Panel discussion »
Pierre-Yves Oudeyer · Marc Bellemare · Peter Stone · Matt Botvinick · Susan Murphy · Anusha Nagabandi · Ashley Edwards · Karen Liu · Pieter Abbeel -
2020 : Contributed Talk #3 »
Hongyu Ren -
2020 : Contributed Talk: Reset-Free Lifelong Learning with Skill-Space Planning »
Kevin Lu · Aditya Grover · Pieter Abbeel · Igor Mordatch -
2020 : Contributed Talk: MaxEnt RL and Robust Control »
Benjamin Eysenbach · Sergey Levine -
2020 : Invited talk - Underfitting and Uncertainty in Self-Supervised Predictive Models »
Chelsea Finn -
2020 : Keynote: Doina Precup »
Doina Precup -
2020 : Climate Change and ML in the Private Sector »
Aisha Walcott-Bryant · Lea Boche · Anima Anandkumar -
2020 Workshop: Deep Reinforcement Learning »
Pieter Abbeel · Chelsea Finn · Joelle Pineau · David Silver · Satinder Singh · Coline Devin · Misha Laskin · Kimin Lee · Janarthanan Rajendran · Vivek Veeriah -
2020 Workshop: Machine Learning and the Physical Sciences »
Anima Anandkumar · Kyle Cranmer · Shirley Ho · Mr. Prabhat · Lenka Zdeborová · Atilim Gunes Baydin · Juan Carrasquilla · Adji Bousso Dieng · Karthik Kashinath · Gilles Louppe · Brian Nord · Michela Paganini · Savannah Thais -
2020 Poster: Open Graph Benchmark: Datasets for Machine Learning on Graphs »
Weihua Hu · Matthias Fey · Marinka Zitnik · Yuxiao Dong · Hongyu Ren · Bowen Liu · Michele Catasta · Jure Leskovec -
2020 Poster: Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning »
Yu Yao · Tongliang Liu · Bo Han · Mingming Gong · Jiankang Deng · Gang Niu · Masashi Sugiyama -
2020 Poster: Part-dependent Label Noise: Towards Instance-dependent Label Noise »
Xiaobo Xia · Tongliang Liu · Bo Han · Nannan Wang · Mingming Gong · Haifeng Liu · Gang Niu · Dacheng Tao · Masashi Sugiyama -
2020 Poster: Weakly-Supervised Reinforcement Learning for Controllable Behavior »
Lisa Lee · Benjamin Eysenbach · Russ Salakhutdinov · Shixiang (Shane) Gu · Chelsea Finn -
2020 Poster: Model Inversion Networks for Model-Based Optimization »
Aviral Kumar · Sergey Levine -
2020 Poster: Graph Information Bottleneck »
Tailin Wu · Hongyu Ren · Pan Li · Jure Leskovec -
2020 Spotlight: Part-dependent Label Noise: Towards Instance-dependent Label Noise »
Xiaobo Xia · Tongliang Liu · Bo Han · Nannan Wang · Mingming Gong · Haifeng Liu · Gang Niu · Dacheng Tao · Masashi Sugiyama -
2020 Spotlight: Open Graph Benchmark: Datasets for Machine Learning on Graphs »
Weihua Hu · Matthias Fey · Marinka Zitnik · Yuxiao Dong · Hongyu Ren · Bowen Liu · Michele Catasta · Jure Leskovec -
2020 Poster: Denoising Diffusion Probabilistic Models »
Jonathan Ho · Ajay Jain · Pieter Abbeel -
2020 Poster: Supervised Contrastive Learning »
Prannay Khosla · Piotr Teterwak · Chen Wang · Aaron Sarna · Yonglong Tian · Phillip Isola · Aaron Maschinot · Ce Liu · Dilip Krishnan -
2020 Poster: What Makes for Good Views for Contrastive Learning? »
Yonglong Tian · Chen Sun · Ben Poole · Dilip Krishnan · Cordelia Schmid · Phillip Isola -
2020 Poster: Munchausen Reinforcement Learning »
Nino Vieillard · Olivier Pietquin · Matthieu Geist -
2020 Poster: Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning »
Nino Vieillard · Tadashi Kozuno · Bruno Scherrer · Olivier Pietquin · Remi Munos · Matthieu Geist -
2020 Poster: Automatic Curriculum Learning through Value Disagreement »
Yunzhi Zhang · Pieter Abbeel · Lerrel Pinto -
2020 Poster: Reward Propagation Using Graph Convolutional Networks »
Martin Klissarov · Doina Precup -
2020 Poster: Continual Learning of Control Primitives : Skill Discovery via Reset-Games »
Kelvin Xu · Siddharth Verma · Chelsea Finn · Sergey Levine -
2020 Poster: Learning compositional functions via multiplicative weight updates »
Jeremy Bernstein · Jiawei Zhao · Markus Meister · Ming-Yu Liu · Anima Anandkumar · Yisong Yue -
2020 Spotlight: Reward Propagation Using Graph Convolutional Networks »
Martin Klissarov · Doina Precup -
2020 Oral: Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning »
Nino Vieillard · Tadashi Kozuno · Bruno Scherrer · Olivier Pietquin · Remi Munos · Matthieu Geist -
2020 Poster: Gradient Surgery for Multi-Task Learning »
Tianhe Yu · Saurabh Kumar · Abhishek Gupta · Sergey Levine · Karol Hausman · Chelsea Finn -
2020 Poster: Rethinking Importance Weighting for Deep Learning under Distribution Shift »
Tongtong Fang · Nan Lu · Gang Niu · Masashi Sugiyama -
2020 Poster: Learning from Aggregate Observations »
Yivan Zhang · Nontawat Charoenphakdee · Zhenguo Wu · Masashi Sugiyama -
2020 Poster: Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring »
Taira Tsuchiya · Junya Honda · Masashi Sugiyama -
2020 Spotlight: Rethinking Importance Weighting for Deep Learning under Distribution Shift »
Tongtong Fang · Nan Lu · Gang Niu · Masashi Sugiyama -
2020 Poster: Auxiliary Task Reweighting for Minimum-data Learning »
Baifeng Shi · Judy Hoffman · Kate Saenko · Trevor Darrell · Huijuan Xu -
2020 Poster: Provably Consistent Partial-Label Learning »
Lei Feng · Jiaqi Lv · Bo Han · Miao Xu · Gang Niu · Xin Geng · Bo An · Masashi Sugiyama -
2020 Poster: Regularizing Black-box Models for Improved Interpretability »
Gregory Plumb · Maruan Al-Shedivat · Ángel Alexander Cabrera · Adam Perer · Eric Xing · Ameet Talwalkar -
2020 Poster: AutoSync: Learning to Synchronize for Data-Parallel Distributed Deep Learning »
Hao Zhang · Yuan Li · Zhijie Deng · Xiaodan Liang · Lawrence Carin · Eric Xing -
2020 Poster: Neural Networks with Recurrent Generative Feedback »
Yujia Huang · James Gornet · Sihui Dai · Zhiding Yu · Tan Nguyen · Doris Tsao · Anima Anandkumar -
2020 Poster: Causal Discovery in Physical Systems from Videos »
Yunzhu Li · Antonio Torralba · Anima Anandkumar · Dieter Fox · Animesh Garg -
2020 Poster: AvE: Assistance via Empowerment »
Yuqing Du · Stas Tiomkin · Emre Kiciman · Daniel Polani · Pieter Abbeel · Anca Dragan -
2020 Poster: Continuous Meta-Learning without Tasks »
James Harrison · Apoorva Sharma · Chelsea Finn · Marco Pavone -
2020 Poster: Improving GAN Training with Probability Ratio Clipping and Sample Reweighting »
Yue Wu · Pan Zhou · Andrew Wilson · Eric Xing · Zhiting Hu -
2020 Poster: Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning »
Weili Nie · Zhiding Yu · Lei Mao · Ankit Patel · Yuke Zhu · Anima Anandkumar -
2020 Spotlight: Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning »
Weili Nie · Zhiding Yu · Lei Mao · Ankit Patel · Yuke Zhu · Anima Anandkumar -
2020 : Research at NVIDIA: New Core AI and Machine Learning Lab »
Anima Anandkumar -
2020 Poster: Space-Time Correspondence as a Contrastive Random Walk »
Allan Jabri · Andrew Owens · Alexei Efros -
2020 Poster: Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement »
Benjamin Eysenbach · XINYANG GENG · Sergey Levine · Russ Salakhutdinov -
2020 Poster: Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators »
Takeshi Teshima · Isao Ishikawa · Koichi Tojo · Kenta Oono · Masahiro Ikeda · Masashi Sugiyama -
2020 Poster: Multipole Graph Neural Operator for Parametric Partial Differential Equations »
Zongyi Li · Nikola Kovachki · Kamyar Azizzadenesheli · Burigede Liu · Andrew Stuart · Kaushik Bhattacharya · Anima Anandkumar -
2020 Poster: Conservative Q-Learning for Offline Reinforcement Learning »
Aviral Kumar · Aurick Zhou · George Tucker · Sergey Levine -
2020 Poster: Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning »
Tabish Rashid · Gregory Farquhar · Bei Peng · Shimon Whiteson -
2020 Poster: Reinforcement Learning with Augmented Data »
Misha Laskin · Kimin Lee · Adam Stooke · Lerrel Pinto · Pieter Abbeel · Aravind Srinivas -
2020 Poster: Generalized Hindsight for Reinforcement Learning »
Alexander Li · Lerrel Pinto · Pieter Abbeel -
2020 Poster: Curriculum By Smoothing »
Samarth Sinha · Animesh Garg · Hugo Larochelle -
2020 Poster: Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning »
Younggyo Seo · Kimin Lee · Ignasi Clavera Gilaberte · Thanard Kurutach · Jinwoo Shin · Pieter Abbeel -
2020 Spotlight: Curriculum By Smoothing »
Samarth Sinha · Animesh Garg · Hugo Larochelle -
2020 Spotlight: Reinforcement Learning with Augmented Data »
Misha Laskin · Kimin Lee · Adam Stooke · Lerrel Pinto · Pieter Abbeel · Aravind Srinivas -
2020 Oral: Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators »
Takeshi Teshima · Isao Ishikawa · Koichi Tojo · Kenta Oono · Masahiro Ikeda · Masashi Sugiyama -
2020 Oral: Space-Time Correspondence as a Contrastive Random Walk »
Allan Jabri · Andrew Owens · Alexei Efros -
2020 Oral: Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement »
Benjamin Eysenbach · XINYANG GENG · Sergey Levine · Russ Salakhutdinov -
2020 Tutorial: (Track3) Offline Reinforcement Learning: From Algorithm Design to Practical Applications Q&A »
Sergey Levine · Aviral Kumar -
2020 Poster: Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver? »
Vitaly Kurin · Saad Godil · Shimon Whiteson · Bryan Catanzaro -
2020 Poster: Gamma-Models: Generative Temporal Difference Learning for Infinite-Horizon Prediction »
Michael Janner · Igor Mordatch · Sergey Levine -
2020 Poster: Learning Retrospective Knowledge with Reverse Reinforcement Learning »
Shangtong Zhang · Vivek Veeriah · Shimon Whiteson -
2020 Poster: Sparse Graphical Memory for Robust Planning »
Scott Emmons · Ajay Jain · Misha Laskin · Thanard Kurutach · Pieter Abbeel · Deepak Pathak -
2020 Poster: One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL »
Saurabh Kumar · Aviral Kumar · Sergey Levine · Chelsea Finn -
2020 Poster: An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay »
Scott Fujimoto · David Meger · Doina Precup -
2020 Poster: Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors »
Karl Pertsch · Oleh Rybkin · Frederik Ebert · Shenghao Zhou · Dinesh Jayaraman · Chelsea Finn · Sergey Levine -
2020 Poster: Fictitious Play for Mean Field Games: Continuous Time Analysis and Applications »
Sarah Perrin · Julien Perolat · Mathieu Lauriere · Matthieu Geist · Romuald Elie · Olivier Pietquin -
2020 Poster: Counterfactual Data Augmentation using Locally Factored Dynamics »
Silviu Pitis · Elliot Creager · Animesh Garg -
2020 Poster: Forethought and Hindsight in Credit Assignment »
Veronica Chelu · Doina Precup · Hado van Hasselt -
2020 Poster: Convolutional Tensor-Train LSTM for Spatio-Temporal Learning »
Jiahao Su · Wonmin Byeon · Jean Kossaifi · Furong Huang · Jan Kautz · Anima Anandkumar -
2020 Poster: Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model »
Alex X. Lee · Anusha Nagabandi · Pieter Abbeel · Sergey Levine -
2020 Session: Orals & Spotlights Track 06: Dynamical Sys/Density/Sparsity »
Animesh Garg · Rose Yu -
2020 Poster: Swapping Autoencoder for Deep Image Manipulation »
Taesung Park · Jun-Yan Zhu · Oliver Wang · Jingwan Lu · Eli Shechtman · Alexei Efros · Richard Zhang -
2020 Poster: Fighting Copycat Agents in Behavioral Cloning from Observation Histories »
Chuan Wen · Jierui Lin · Trevor Darrell · Dinesh Jayaraman · Yang Gao -
2020 Poster: Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design »
Michael Dennis · Natasha Jaques · Eugene Vinitsky · Alexandre Bayen · Stuart Russell · Andrew Critch · Sergey Levine -
2020 Poster: MOPO: Model-based Offline Policy Optimization »
Tianhe Yu · Garrett Thomas · Lantao Yu · Stefano Ermon · James Zou · Sergey Levine · Chelsea Finn · Tengyu Ma -
2020 Poster: DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction »
Aviral Kumar · Abhishek Gupta · Sergey Levine -
2020 Poster: Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs »
Hongyu Ren · Jure Leskovec -
2020 Poster: Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems »
Sahin Lale · Kamyar Azizzadenesheli · Babak Hassibi · Anima Anandkumar -
2020 Spotlight: DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction »
Aviral Kumar · Abhishek Gupta · Sergey Levine -
2020 Oral: Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design »
Michael Dennis · Natasha Jaques · Eugene Vinitsky · Alexandre Bayen · Stuart Russell · Andrew Critch · Sergey Levine -
2020 : Discussion Panel: Hugo Larochelle, Finale Doshi-Velez, Devi Parikh, Marc Deisenroth, Julien Mairal, Katja Hofmann, Phillip Isola, and Michael Bowling »
Hugo Larochelle · Finale Doshi-Velez · Marc Deisenroth · Devi Parikh · Julien Mairal · Katja Hofmann · Phillip Isola · Michael Bowling -
2020 : Prof. Anima Anandkumar (California Institute of Technology and NVIDIA) »
Anima Anandkumar -
2020 Tutorial: (Track3) Offline Reinforcement Learning: From Algorithm Design to Practical Applications »
Sergey Levine · Aviral Kumar -
2019 : Panel Session: A new hope for neuroscience »
Yoshua Bengio · Blake Richards · Timothy Lillicrap · Ila Fiete · David Sussillo · Doina Precup · Konrad Kording · Surya Ganguli -
2019 : Contributed Session - Spotlight Talks »
Jonathan Frankle · David Schwab · Ari Morcos · Qianli Ma · Yao-Hung Hubert Tsai · Ruslan Salakhutdinov · YiDing Jiang · Dilip Krishnan · Hossein Mobahi · Samy Bengio · Sho Yaida · Muqiao Yang -
2019 : Poster Session »
Pravish Sainath · Mohamed Akrout · Charles Delahunt · Nathan Kutz · Guangyu Robert Yang · Joseph Marino · L F Abbott · Nicolas Vecoven · Damien Ernst · andrew warrington · Michael Kagan · Kyunghyun Cho · Kameron Harris · Leopold Grinberg · John J. Hopfield · Dmitry Krotov · Taliah Muhammad · Erick Cobos · Edgar Walker · Jacob Reimer · Andreas Tolias · Alexander Ecker · Janaki Sheth · Yu Zhang · Maciej Wołczyk · Jacek Tabor · Szymon Maszke · Roman Pogodin · Dane Corneil · Wulfram Gerstner · Baihan Lin · Guillermo Cecchi · Jenna M Reinen · Irina Rish · Guillaume Bellec · Darjan Salaj · Anand Subramoney · Wolfgang Maass · Yueqi Wang · Ari Pakman · Jin Hyung Lee · Liam Paninski · Bryan Tripp · Colin Graber · Alex Schwing · Luke Prince · Gabriel Ocker · Michael Buice · Benjamin Lansdell · Konrad Kording · Jack Lindsey · Terrence Sejnowski · Matthew Farrell · Eric Shea-Brown · Nicolas Farrugia · Victor Nepveu · Jiwoong Im · Kristin Branson · Brian Hu · Ramakrishnan Iyer · Stefan Mihalas · Sneha Aenugu · Hananel Hazan · Sihui Dai · Tan Nguyen · Doris Tsao · Richard Baraniuk · Anima Anandkumar · Hidenori Tanaka · Aran Nayebi · Stephen Baccus · Surya Ganguli · Dean Pospisil · Eilif Muller · Jeffrey S Cheng · Gaël Varoquaux · Kamalaker Dadi · Dimitrios C Gklezakos · Rajesh PN Rao · Anand Louis · Christos Papadimitriou · Santosh Vempala · Naganand Yadati · Daniel Zdeblick · Daniela M Witten · Nicholas Roberts · Vinay Prabhu · Pierre Bellec · Poornima Ramesh · Jakob H Macke · Santiago Cadena · Guillaume Bellec · Franz Scherr · Owen Marschall · Robert Kim · Hannes Rapp · Marcio Fonseca · Oliver Armitage · Jiwoong Im · Thomas Hardcastle · Abhishek Sharma · Wyeth Bair · Adrian Valente · Shane Shang · Merav Stern · Rutuja Patil · Peter Wang · Sruthi Gorantla · Peter Stratton · Tristan Edwards · Jialin Lu · Martin Ester · Yurii Vlasov · Siavash Golkar -
2019 : Panel - The Role of Communication at Large: Aparna Lakshmiratan, Jason Yosinski, Been Kim, Surya Ganguli, Finale Doshi-Velez »
Aparna Lakshmiratan · Finale Doshi-Velez · Surya Ganguli · Zachary Lipton · Michela Paganini · Anima Anandkumar · Jason Yosinski -
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 : Poster Session »
Matthia Sabatelli · Adam Stooke · Amir Abdi · Paulo Rauber · Leonard Adolphs · Ian Osband · Hardik Meisheri · Karol Kurach · Johannes Ackermann · Matt Benatan · GUO ZHANG · Chen Tessler · Dinghan Shen · Mikayel Samvelyan · Riashat Islam · Murtaza Dalal · Luke Harries · Andrey Kurenkov · Konrad Żołna · Sudeep Dasari · Kristian Hartikainen · Ofir Nachum · Kimin Lee · Markus Holzleitner · Vu Nguyen · Francis Song · Christopher Grimm · Felipe Leno da Silva · Yuping Luo · Yifan Wu · Alex Lee · Thomas Paine · Wei-Yang Qu · Daniel Graves · Yannis Flet-Berliac · Yunhao Tang · Suraj Nair · Matthew Hausknecht · Akhil Bagaria · Simon Schmitt · Bowen Baker · Paavo Parmas · Benjamin Eysenbach · Lisa Lee · Siyu Lin · Daniel Seita · Abhishek Gupta · Riley Simmons-Edler · Yijie Guo · Kevin Corder · Vikash Kumar · Scott Fujimoto · Adam Lerer · Ignasi Clavera Gilaberte · Nicholas Rhinehart · Ashvin Nair · Ge Yang · Lingxiao Wang · Sungryull Sohn · J. Fernando Hernandez-Garcia · Xian Yeow Lee · Rupesh Srivastava · Khimya Khetarpal · Chenjun Xiao · Luckeciano Carvalho Melo · Rishabh Agarwal · Tianhe Yu · Glen Berseth · Devendra Singh Chaplot · Jie Tang · Anirudh Srinivasan · Tharun Kumar Reddy Medini · Aaron Havens · Misha Laskin · Asier Mujika · Rohan Saphal · Joseph Marino · Alex Ray · Joshua Achiam · Ajay Mandlekar · Zhuang Liu · Danijar Hafner · Zhiwen Tang · Ted Xiao · Michael Walton · Jeff Druce · Ferran Alet · Zhang-Wei Hong · Stephanie Chan · Anusha Nagabandi · Hao Liu · Hao Sun · Ge Liu · Dinesh Jayaraman · John Co-Reyes · Sophia Sanborn -
2019 : Oral Presentations »
Janith Petangoda · Sergio Pascual-Diaz · Jordi Grau-Moya · Raphaël Marinier · Olivier Pietquin · Alexei Efros · Phillip Isola · Trevor Darrell · Christopher Lu · Deepak Pathak · Johan Ferret -
2019 : Lunch Break and Posters »
Xingyou Song · Elad Hoffer · Wei-Cheng Chang · Jeremy Cohen · Jyoti Islam · Yaniv Blumenfeld · Andreas Madsen · Jonathan Frankle · Sebastian Goldt · Satrajit Chatterjee · Abhishek Panigrahi · Alex Renda · Brian Bartoldson · Israel Birhane · Aristide Baratin · Niladri Chatterji · Roman Novak · Jessica Forde · YiDing Jiang · Yilun Du · Linara Adilova · Michael Kamp · Berry Weinstein · Itay Hubara · Tal Ben-Nun · Torsten Hoefler · Daniel Soudry · Hsiang-Fu Yu · Kai Zhong · Yiming Yang · Inderjit Dhillon · Jaime Carbonell · Yanqing Zhang · Dar Gilboa · Johannes Brandstetter · Alexander R Johansen · Gintare Karolina Dziugaite · Raghav Somani · Ari Morcos · Freddie Kalaitzis · Hanie Sedghi · Lechao Xiao · John Zech · Muqiao Yang · Simran Kaur · Qianli Ma · Yao-Hung Hubert Tsai · Ruslan Salakhutdinov · Sho Yaida · Zachary Lipton · Daniel Roy · Michael Carbin · Florent Krzakala · Lenka Zdeborová · Guy Gur-Ari · Ethan Dyer · Dilip Krishnan · Hossein Mobahi · Samy Bengio · Behnam Neyshabur · Praneeth Netrapalli · Kris Sankaran · Julien Cornebise · Yoshua Bengio · Vincent Michalski · Samira Ebrahimi Kahou · Md Rifat Arefin · Jiri Hron · Jaehoon Lee · Jascha Sohl-Dickstein · Samuel Schoenholz · David Schwab · Dongyu Li · Sang Keun Choe · Henning Petzka · Ashish Verma · Zhichao Lin · Cristian Sminchisescu -
2019 : Poster Spotlight 2 »
Aaron Sidford · Mengdi Wang · Lin Yang · Yinyu Ye · Zuyue Fu · Zhuoran Yang · Yongxin Chen · Zhaoran Wang · Ofir Nachum · Bo Dai · Ilya Kostrikov · Dale Schuurmans · Ziyang Tang · Yihao Feng · Lihong Li · Denny Zhou · Qiang Liu · Rodrigo Toro Icarte · Ethan Waldie · Toryn Klassen · Rick Valenzano · Margarita Castro · Simon Du · Sham Kakade · Ruosong Wang · Minshuo Chen · Tianyi Liu · Xingguo Li · Zhaoran Wang · Tuo Zhao · Philip Amortila · Doina Precup · Prakash Panangaden · Marc Bellemare -
2019 : Panel Discussion »
Richard Sutton · Doina Precup -
2019 : Bayes-Adaptive Deep Reinforcement Learning via Meta-Learning - Invited Talk »
Shimon Whiteson -
2019 : Poster and Coffee Break 1 »
Aaron Sidford · Aditya Mahajan · Alejandro Ribeiro · Alex Lewandowski · Ali H Sayed · Ambuj Tewari · Angelika Steger · Anima Anandkumar · Asier Mujika · Hilbert J Kappen · Bolei Zhou · Byron Boots · Chelsea Finn · Chen-Yu Wei · Chi Jin · Ching-An Cheng · Christina Yu · Clement Gehring · Craig Boutilier · Dahua Lin · Daniel McNamee · Daniel Russo · David Brandfonbrener · Denny Zhou · Devesh Jha · Diego Romeres · Doina Precup · Dominik Thalmeier · Eduard Gorbunov · Elad Hazan · Elena Smirnova · Elvis Dohmatob · Emma Brunskill · Enrique Munoz de Cote · Ethan Waldie · Florian Meier · Florian Schaefer · Ge Liu · Gergely Neu · Haim Kaplan · Hao Sun · Hengshuai Yao · Jalaj Bhandari · James A Preiss · Jayakumar Subramanian · Jiajin Li · Jieping Ye · Jimmy Smith · Joan Bas Serrano · Joan Bruna · John Langford · Jonathan Lee · Jose A. Arjona-Medina · Kaiqing Zhang · Karan Singh · Yuping Luo · Zafarali Ahmed · Zaiwei Chen · Zhaoran Wang · Zhizhong Li · Zhuoran Yang · Ziping Xu · Ziyang Tang · Yi Mao · David Brandfonbrener · Shirli Di-Castro · Riashat Islam · Zuyue Fu · Abhishek Naik · Saurabh Kumar · Benjamin Petit · Angeliki Kamoutsi · Simone Totaro · Arvind Raghunathan · Rui Wu · Donghwan Lee · Dongsheng Ding · Alec Koppel · Hao Sun · Christian Tjandraatmadja · Mahdi Karami · Jincheng Mei · Chenjun Xiao · Junfeng Wen · Zichen Zhang · Ross Goroshin · Mohammad Pezeshki · Jiaqi Zhai · Philip Amortila · Shuo Huang · Mariya Vasileva · El houcine Bergou · Adel Ahmadyan · Haoran Sun · Sheng Zhang · Lukas Gruber · Yuanhao Wang · Tetiana Parshakova -
2019 : Opening Remarks »
Manzil Zaheer · Nicholas Monath · Ari Kobren · Junier Oliva · Barnabas Poczos · Ruslan Salakhutdinov · Andrew McCallum -
2019 : Invited Talk: Hierarchical Reinforcement Learning: Computational Advances and Neuroscience Connections »
Doina Precup -
2019 : Opening Remarks »
Atilim Gunes Baydin · Juan Carrasquilla · Shirley Ho · Karthik Kashinath · Michela Paganini · Savannah Thais · Anima Anandkumar · Kyle Cranmer · Roger Melko · Mr. Prabhat · Frank Wood -
2019 Workshop: Machine Learning and the Physical Sciences »
Atilim Gunes Baydin · Juan Carrasquilla · Shirley Ho · Karthik Kashinath · Michela Paganini · Savannah Thais · Anima Anandkumar · Kyle Cranmer · Roger Melko · Mr. Prabhat · Frank Wood -
2019 Workshop: Deep Reinforcement Learning »
Pieter Abbeel · Chelsea Finn · Joelle Pineau · David Silver · Satinder Singh · Joshua Achiam · Carlos Florensa · Christopher Grimm · Haoran Tang · Vivek Veeriah -
2019 Workshop: Sets and Partitions »
Nicholas Monath · Manzil Zaheer · Andrew McCallum · Ari Kobren · Junier Oliva · Barnabas Poczos · Ruslan Salakhutdinov -
2019 : Panel Discussion »
Linda Smith · Josh Tenenbaum · Lisa Anne Hendricks · James McClelland · Timothy Lillicrap · Jesse Thomason · Jason Baldridge · Louis-Philippe Morency -
2019 : Panel Discussion led by Grace Lindsay »
Grace Lindsay · Blake Richards · Doina Precup · Jacqueline Gottlieb · Jeff Clune · Jane Wang · Richard Sutton · Angela Yu · Ida Momennejad -
2019 : Pieter Abbeel »
Pieter Abbeel -
2019 : Poster Session »
Ethan Harris · Tom White · Oh Hyeon Choung · Takashi Shinozaki · Dipan Pal · Katherine L. Hermann · Judy Borowski · Camilo Fosco · Chaz Firestone · Vijay Veerabadran · Benjamin Lahner · Chaitanya Ryali · Fenil Doshi · Pulkit Singh · Sharon Zhou · Michel Besserve · Michael Chang · Anelise Newman · Mahesan Niranjan · Jonathon Hare · Daniela Mihai · Marios Savvides · Simon Kornblith · Christina M Funke · Aude Oliva · Virginia de Sa · Dmitry Krotov · Colin Conwell · George Alvarez · Alex Kolchinski · Shengjia Zhao · Mitchell Gordon · Michael Bernstein · Stefano Ermon · Arash Mehrjou · Bernhard Schölkopf · John Co-Reyes · Michael Janner · Jiajun Wu · Josh Tenenbaum · Sergey Levine · Yalda Mohsenzadeh · Zhenglong Zhou -
2019 : Poster Session »
Rishav Chourasia · Yichong Xu · Corinna Cortes · Chien-Yi Chang · Yoshihiro Nagano · So Yeon Min · Benedikt Boecking · Phi Vu Tran · Kamyar Ghasemipour · Qianggang Ding · Shouvik Mani · Vikram Voleti · Rasool Fakoor · Miao Xu · Kenneth Marino · Lisa Lee · Volker Tresp · Jean-Francois Kagy · Marvin Zhang · Barnabas Poczos · Dinesh Khandelwal · Adrien Bardes · Evan Shelhamer · Jiacheng Zhu · Ziming Li · Xiaoyan Li · Dmitrii Krasheninnikov · Ruohan Wang · Mayoore Jaiswal · Emad Barsoum · Suvansh Sanjeev · Theeraphol Wattanavekin · Qizhe Xie · Sifan Wu · Yuki Yoshida · David Kanaa · Sina Khoshfetrat Pakazad · Mehdi Maasoumy -
2019 : Why language understanding is not a solved problem »
James McClelland -
2019 : Coffee/Poster session 1 »
Shiro Takagi · Khurram Javed · Johanna Sommer · Amr Sharaf · Pierluca D'Oro · Ying Wei · Sivan Doveh · Colin White · Santiago Gonzalez · Cuong Nguyen · Mao Li · Tianhe Yu · Tiago Ramalho · Masahiro Nomura · Ahsan Alvi · Jean-Francois Ton · W. Ronny Huang · Jessica Lee · Sebastian Flennerhag · Michael Zhang · Abram Friesen · Paul Blomstedt · Alina Dubatovka · Sergey Bartunov · Subin Yi · Iaroslav Shcherbatyi · Christian Simon · Zeyuan Shang · David MacLeod · Lu Liu · Liam Fowl · Diego Mesquita · Deirdre Quillen -
2019 : Coffee Break & Poster Session »
Samia Mohinta · Andrea Agostinelli · Alexandra Moringen · Jee Hang Lee · Yat Long Lo · Wolfgang Maass · Blue Sheffer · Colin Bredenberg · Benjamin Eysenbach · Liyu Xia · Efstratios Markou · Jan Lichtenberg · Pierre Richemond · Tony Zhang · JB Lanier · Baihan Lin · William Fedus · Glen Berseth · Marta Sarrico · Matthew Crosby · Stephen McAleer · Sina Ghiassian · Franz Scherr · Guillaume Bellec · Darjan Salaj · Arinbjörn Kolbeinsson · Matthew Rosenberg · Jaehoon Shin · Sang Wan Lee · Guillermo Cecchi · Irina Rish · Elias Hajek -
2019 : Opening Remarks »
Raymond Chua · Feryal Behbahani · Sara Zannone · Rui Ponte Costa · Claudia Clopath · Doina Precup · Blake Richards -
2019 Workshop: AI for Humanitarian Assistance and Disaster Response »
Ritwik Gupta · Robin Murphy · Trevor Darrell · Eric Heim · Zhangyang Wang · Bryce Goodman · Piotr Biliński -
2019 Workshop: Biological and Artificial Reinforcement Learning »
Raymond Chua · Sara Zannone · Feryal Behbahani · Rui Ponte Costa · Claudia Clopath · Blake Richards · Doina Precup -
2019 Workshop: Learning with Rich Experience: Integration of Learning Paradigms »
Zhiting Hu · Andrew Wilson · Chelsea Finn · Lisa Lee · Taylor Berg-Kirkpatrick · Ruslan Salakhutdinov · Eric Xing -
2019 Poster: Wasserstein Dependency Measure for Representation Learning »
Sherjil Ozair · Corey Lynch · Yoshua Bengio · Aaron van den Oord · Sergey Levine · Pierre Sermanet -
2019 Poster: Evaluating Protein Transfer Learning with TAPE »
Roshan Rao · Nicholas Bhattacharya · Neil Thomas · Yan Duan · Peter Chen · John Canny · Pieter Abbeel · Yun Song -
2019 Spotlight: Evaluating Protein Transfer Learning with TAPE »
Roshan Rao · Nicholas Bhattacharya · Neil Thomas · Yan Duan · Peter Chen · John Canny · Pieter Abbeel · Yun Song -
2019 Poster: Competitive Gradient Descent »
Florian Schaefer · Anima Anandkumar -
2019 Poster: Learning Robust Global Representations by Penalizing Local Predictive Power »
Haohan Wang · Songwei Ge · Zachary Lipton · Eric Xing -
2019 Poster: Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck »
Maximilian Igl · Kamil Ciosek · Yingzhen Li · Sebastian Tschiatschek · Cheng Zhang · Sam Devlin · Katja Hofmann -
2019 Poster: Planning with Goal-Conditioned Policies »
Soroush Nasiriany · Vitchyr Pong · Steven Lin · Sergey Levine -
2019 Poster: Search on the Replay Buffer: Bridging Planning and Reinforcement Learning »
Benjamin Eysenbach · Russ Salakhutdinov · Sergey Levine -
2019 Poster: Goal-conditioned Imitation Learning »
Yiming Ding · Carlos Florensa · Pieter Abbeel · Mariano Phielipp -
2019 Poster: Learning Data Manipulation for Augmentation and Weighting »
Zhiting Hu · Bowen Tan · Russ Salakhutdinov · Tom Mitchell · Eric Xing -
2019 Poster: Geometry-Aware Neural Rendering »
Joshua Tobin · Wojciech Zaremba · Pieter Abbeel -
2019 Poster: Learning Sample-Specific Models with Low-Rank Personalized Regression »
Ben Lengerich · Bryon Aragam · Eric Xing -
2019 Poster: MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies »
Xue Bin Peng · Michael Chang · Grace Zhang · Pieter Abbeel · Sergey Levine -
2019 Poster: MAVEN: Multi-Agent Variational Exploration »
Anuj Mahajan · Tabish Rashid · Mikayel Samvelyan · Shimon Whiteson -
2019 Poster: Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Gradient Estimators for Reinforcement Learning »
Gregory Farquhar · Shimon Whiteson · Jakob Foerster -
2019 Poster: Budgeted Reinforcement Learning in Continuous State Space »
Nicolas Carrara · Edouard Leurent · Romain Laroche · Tanguy Urvoy · Odalric-Ambrym Maillard · Olivier Pietquin -
2019 Poster: Multi-Agent Common Knowledge Reinforcement Learning »
Christian Schroeder de Witt · Jakob Foerster · Gregory Farquhar · Philip Torr · Wendelin Boehmer · Shimon Whiteson -
2019 Poster: Meta-Inverse Reinforcement Learning with Probabilistic Context Variables »
Lantao Yu · Tianhe Yu · Chelsea Finn · Stefano Ermon -
2019 Poster: Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction »
Aviral Kumar · Justin Fu · George Tucker · Sergey Levine -
2019 Poster: Unsupervised Curricula for Visual Meta-Reinforcement Learning »
Allan Jabri · Kyle Hsu · Abhishek Gupta · Benjamin Eysenbach · Sergey Levine · Chelsea Finn -
2019 Oral: Geometry-Aware Neural Rendering »
Joshua Tobin · Wojciech Zaremba · Pieter Abbeel -
2019 Poster: DAC: The Double Actor-Critic Architecture for Learning Options »
Shangtong Zhang · Shimon Whiteson -
2019 Poster: Uncoupled Regression from Pairwise Comparison Data »
Ritsugen Jo · Junya Honda · Gang Niu · Masashi Sugiyama -
2019 Poster: Fast Efficient Hyperparameter Tuning for Policy Gradient Methods »
Supratik Paul · Vitaly Kurin · Shimon Whiteson -
2019 Poster: Are Anchor Points Really Indispensable in Label-Noise Learning? »
Xiaobo Xia · Tongliang Liu · Nannan Wang · Bo Han · Chen Gong · Gang Niu · Masashi Sugiyama -
2019 Poster: Language as an Abstraction for Hierarchical Deep Reinforcement Learning »
YiDing Jiang · Shixiang (Shane) Gu · Kevin Murphy · Chelsea Finn -
2019 Poster: Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks »
Sitao Luan · Mingde Zhao · Xiao-Wen Chang · Doina Precup -
2019 Poster: Compositional Plan Vectors »
Coline Devin · Daniel Geng · Pieter Abbeel · Trevor Darrell · Sergey Levine -
2019 Poster: VIREL: A Variational Inference Framework for Reinforcement Learning »
Mattie Fellows · Anuj Mahajan · Tim G. J. Rudner · Shimon Whiteson -
2019 Spotlight: Unsupervised Curricula for Visual Meta-Reinforcement Learning »
Allan Jabri · Kyle Hsu · Abhishek Gupta · Benjamin Eysenbach · Sergey Levine · Chelsea Finn -
2019 Spotlight: VIREL: A Variational Inference Framework for Reinforcement Learning »
Mattie Fellows · Anuj Mahajan · Tim G. J. Rudner · Shimon Whiteson -
2019 Poster: Causal Confusion in Imitation Learning »
Pim de Haan · Dinesh Jayaraman · Sergey Levine -
2019 Poster: Meta-Learning with Implicit Gradients »
Aravind Rajeswaran · Chelsea Finn · Sham Kakade · Sergey Levine -
2019 Poster: On the Utility of Learning about Humans for Human-AI Coordination »
Micah Carroll · Rohin Shah · Mark Ho · Tom Griffiths · Sanjit Seshia · Pieter Abbeel · Anca Dragan -
2019 Poster: When to Trust Your Model: Model-Based Policy Optimization »
Michael Janner · Justin Fu · Marvin Zhang · Sergey Levine -
2019 Poster: Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity »
Deepak Pathak · Christopher Lu · Trevor Darrell · Phillip Isola · Alexei Efros -
2019 Poster: Compression with Flows via Local Bits-Back Coding »
Jonathan Ho · Evan Lohn · Pieter Abbeel -
2019 Poster: Guided Meta-Policy Search »
Russell Mendonca · Abhishek Gupta · Rosen Kralev · Pieter Abbeel · Sergey Levine · Chelsea Finn -
2019 Spotlight: Compression with Flows via Local Bits-Back Coding »
Jonathan Ho · Evan Lohn · Pieter Abbeel -
2019 Spotlight: Guided Meta-Policy Search »
Russell Mendonca · Abhishek Gupta · Rosen Kralev · Pieter Abbeel · Sergey Levine · Chelsea Finn -
2019 Spotlight: Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity »
Deepak Pathak · Christopher Lu · Trevor Darrell · Phillip Isola · Alexei Efros -
2019 Oral: Causal Confusion in Imitation Learning »
Pim de Haan · Dinesh Jayaraman · Sergey Levine -
2019 Poster: Generalized Off-Policy Actor-Critic »
Shangtong Zhang · Wendelin Boehmer · Shimon Whiteson -
2019 Poster: On the Calibration of Multiclass Classification with Rejection »
Chenri Ni · Nontawat Charoenphakdee · Junya Honda · Masashi Sugiyama -
2019 Poster: A Unified Bellman Optimality Principle Combining Reward Maximization and Empowerment »
Felix Leibfried · Sergio Pascual-Díaz · Jordi Grau-Moya -
2018 : Poster Session »
Sujay Sanghavi · Vatsal Shah · Yanyao Shen · Tianchen Zhao · Yuandong Tian · Tomer Galanti · Mufan Li · Gilad Cohen · Daniel Rothchild · Aristide Baratin · Devansh Arpit · Vagelis Papalexakis · Michael Perlmutter · Ashok Vardhan Makkuva · Pim de Haan · Yingyan Lin · Wanmo Kang · Cheolhyoung Lee · Hao Shen · Sho Yaida · Dan Roberts · Nadav Cohen · Philippe Casgrain · Dejiao Zhang · Tengyu Ma · Avinash Ravichandran · Julian Emilio Salazar · Bo Li · Davis Liang · Christopher Wong · Glen Bigan Mbeng · Animesh Garg -
2018 : Spotlights 2 »
Aditya Gopalan · Sungjoon Choi · Thomas Ringstrom · Roy Fox · Jonas Degrave · Xiya Cao · Karl Pertsch · Maximilian Igl · Brian Ichter -
2018 : Meta-Learning to Follow Instructions, Examples, and Demonstrations »
Sergey Levine -
2018 : Poster Session 1 »
Kyle H Ambert · Brandon Araki · Xiya Cao · Sungjoon Choi · Hao(Jackson) Cui · Jonas Degrave · Yaqi Duan · Mattie Fellows · Carlos Florensa · Karan Goel · Aditya Gopalan · Ming-Xu Huang · Jonathan Hunt · Cyril Ibrahim · Brian Ichter · Maximilian Igl · Zheng Tracy Ke · Igor Kiselev · Anuj Mahajan · Arash Mehrjou · Karl Pertsch · Alexandre Piche · Nicholas Rhinehart · Thomas Ringstrom · Reazul Hasan Russel · Oleh Rybkin · Ion Stoica · Sharad Vikram · Angelina Wang · Ting-Han Wei · Abigail H Wen · I-Chen Wu · Zhengwei Wu · Linhai Xie · Dinghan Shen -
2018 : Spotlight Talks I »
Juan Leni · Michael Spranger · Ben Bogin · Shane Steinert-Threlkeld · Nicholas Tomlin · Fushan Li · Michael Noukhovitch · Tushar Jain · Jason Lee · Yen-Ling Kuo · Josefina Correa · Karol Hausman -
2018 : Pieter Abbeel »
Pieter Abbeel -
2018 : TBA 2 »
Sergey Levine -
2018 : Control as Inference and Soft Deep RL (Sergey Levine) »
Sergey Levine -
2018 Workshop: Integration of Deep Learning Theories »
Richard Baraniuk · Anima Anandkumar · Stephane Mallat · Ankit Patel · nhật Hồ -
2018 : TBC 9 »
Sergey Levine -
2018 : Coffee Break and Poster Session I »
Pim de Haan · Bin Wang · Dequan Wang · Aadil Hayat · Ibrahim Sobh · Muhammad Asif Rana · Thibault Buhet · Nicholas Rhinehart · Arjun Sharma · Alex Bewley · Michael Kelly · Lionel Blondé · Ozgur S. Oguz · Vaibhav Viswanathan · Jeroen Vanbaar · Konrad Żołna · Negar Rostamzadeh · Rowan McAllister · Sanjay Thakur · Alexandros Kalousis · Chelsea Sidrane · Sujoy Paul · Daphne Chen · Michal Garmulewicz · Henryk Michalewski · Coline Devin · Hongyu Ren · Jiaming Song · Wen Sun · Hanzhang Hu · Wulong Liu · Emilie Wirbel -
2018 Workshop: Deep Reinforcement Learning »
Pieter Abbeel · David Silver · Satinder Singh · Joelle Pineau · Joshua Achiam · Rein Houthooft · Aravind Srinivas -
2018 Workshop: Visually grounded interaction and language »
Florian Strub · Harm de Vries · Erik Wijmans · Samyak Datta · Ethan Perez · Mateusz Malinowski · Stefan Lee · Peter Anderson · Aaron Courville · Jeremie MARY · Dhruv Batra · Devi Parikh · Olivier Pietquin · Chiori HORI · Tim Marks · Anoop Cherian -
2018 Poster: Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning »
Supasorn Suwajanakorn · Noah Snavely · Jonathan Tompson · Mohammad Norouzi -
2018 Poster: Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models »
Kurtland Chua · Roberto Calandra · Rowan McAllister · Sergey Levine -
2018 Poster: Binary Classification from Positive-Confidence Data »
Takashi Ishida · Gang Niu · Masashi Sugiyama -
2018 Poster: The Sample Complexity of Semi-Supervised Learning with Nonparametric Mixture Models »
Chen Dan · Liu Leqi · Bryon Aragam · Pradeep Ravikumar · Eric Xing -
2018 Spotlight: Binary Classification from Positive-Confidence Data »
Takashi Ishida · Gang Niu · Masashi Sugiyama -
2018 Spotlight: Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models »
Kurtland Chua · Roberto Calandra · Rowan McAllister · Sergey Levine -
2018 Oral: Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning »
Supasorn Suwajanakorn · Noah Snavely · Jonathan Tompson · Mohammad Norouzi -
2018 Poster: Symbolic Graph Reasoning Meets Convolutions »
Xiaodan Liang · Zhiting Hu · Hao Zhang · Liang Lin · Eric Xing -
2018 Poster: Probabilistic Model-Agnostic Meta-Learning »
Chelsea Finn · Kelvin Xu · Sergey Levine -
2018 Poster: Uplift Modeling from Separate Labels »
Ikko Yamane · Florian Yger · Jamal Atif · Masashi Sugiyama -
2018 Poster: Temporal Regularization for Markov Decision Process »
Pierre Thodoroff · Audrey Durand · Joelle Pineau · Doina Precup -
2018 Poster: Representation Balancing MDPs for Off-policy Policy Evaluation »
Yao Liu · Omer Gottesman · Aniruddh Raghu · Matthieu Komorowski · Aldo Faisal · Finale Doshi-Velez · Emma Brunskill -
2018 Poster: Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces »
Motoya Ohnishi · Masahiro Yukawa · Mikael Johansson · Masashi Sugiyama -
2018 Poster: Meta-Reinforcement Learning of Structured Exploration Strategies »
Abhishek Gupta · Russell Mendonca · YuXuan Liu · Pieter Abbeel · Sergey Levine -
2018 Poster: Multi-Agent Generative Adversarial Imitation Learning »
Jiaming Song · Hongyu Ren · Dorsa Sadigh · Stefano Ermon -
2018 Poster: Learning Plannable Representations with Causal InfoGAN »
Thanard Kurutach · Aviv Tamar · Ge Yang · Stuart Russell · Pieter Abbeel -
2018 Poster: Visual Reinforcement Learning with Imagined Goals »
Ashvin Nair · Vitchyr Pong · Murtaza Dalal · Shikhar Bahl · Steven Lin · Sergey Levine -
2018 Poster: DAGs with NO TEARS: Continuous Optimization for Structure Learning »
Xun Zheng · Bryon Aragam · Pradeep Ravikumar · Eric Xing -
2018 Spotlight: DAGs with NO TEARS: Continuous Optimization for Structure Learning »
Xun Zheng · Bryon Aragam · Pradeep Ravikumar · Eric Xing -
2018 Spotlight: Visual Reinforcement Learning with Imagined Goals »
Ashvin Nair · Vitchyr Pong · Murtaza Dalal · Shikhar Bahl · Steven Lin · Sergey Levine -
2018 Spotlight: Meta-Reinforcement Learning of Structured Exploration Strategies »
Abhishek Gupta · Russell Mendonca · YuXuan Liu · Pieter Abbeel · Sergey Levine -
2018 Poster: Learning Pipelines with Limited Data and Domain Knowledge: A Study in Parsing Physics Problems »
Mrinmaya Sachan · Kumar Avinava Dubey · Tom Mitchell · Dan Roth · Eric Xing -
2018 Poster: Visual Memory for Robust Path Following »
Ashish Kumar · Saurabh Gupta · David Fouhey · Sergey Levine · Jitendra Malik -
2018 Poster: Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks »
Yusuke Tsuzuku · Issei Sato · Masashi Sugiyama -
2018 Poster: Variational Inverse Control with Events: A General Framework for Data-Driven Reward Definition »
Justin Fu · Avi Singh · Dibya Ghosh · Larry Yang · Sergey Levine -
2018 Poster: Learning Safe Policies with Expert Guidance »
Jessie Huang · Fa Wu · Doina Precup · Yang Cai -
2018 Poster: Deep Generative Models with Learnable Knowledge Constraints »
Zhiting Hu · Zichao Yang · Russ Salakhutdinov · LIANHUI Qin · Xiaodan Liang · Haoye Dong · Eric Xing -
2018 Oral: Visual Memory for Robust Path Following »
Ashish Kumar · Saurabh Gupta · David Fouhey · Sergey Levine · Jitendra Malik -
2018 Poster: Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation »
Yuan Li · Xiaodan Liang · Zhiting Hu · Eric Xing -
2018 Poster: Neural Architecture Search with Bayesian Optimisation and Optimal Transport »
Kirthevasan Kandasamy · Willie Neiswanger · Jeff Schneider · Barnabas Poczos · Eric Xing -
2018 Poster: Data-Efficient Hierarchical Reinforcement Learning »
Ofir Nachum · Shixiang (Shane) Gu · Honglak Lee · Sergey Levine -
2018 Poster: Speaker-Follower Models for Vision-and-Language Navigation »
Daniel Fried · Ronghang Hu · Volkan Cirik · Anna Rohrbach · Jacob Andreas · Louis-Philippe Morency · Taylor Berg-Kirkpatrick · Kate Saenko · Dan Klein · Trevor Darrell -
2018 Poster: Evolved Policy Gradients »
Rein Houthooft · Yuhua Chen · Phillip Isola · Bradly Stadie · Filip Wolski · OpenAI Jonathan Ho · Pieter Abbeel -
2018 Poster: Masking: A New Perspective of Noisy Supervision »
Bo Han · Jiangchao Yao · Gang Niu · Mingyuan Zhou · Ivor Tsang · Ya Zhang · Masashi Sugiyama -
2018 Poster: Bias and Generalization in Deep Generative Models: An Empirical Study »
Shengjia Zhao · Hongyu Ren · Arianna Yuan · Jiaming Song · Noah Goodman · Stefano Ermon -
2018 Spotlight: Neural Architecture Search with Bayesian Optimisation and Optimal Transport »
Kirthevasan Kandasamy · Willie Neiswanger · Jeff Schneider · Barnabas Poczos · Eric Xing -
2018 Spotlight: Bias and Generalization in Deep Generative Models: An Empirical Study »
Shengjia Zhao · Hongyu Ren · Arianna Yuan · Jiaming Song · Noah Goodman · Stefano Ermon -
2018 Spotlight: Evolved Policy Gradients »
Rein Houthooft · Yuhua Chen · Phillip Isola · Bradly Stadie · Filip Wolski · OpenAI Jonathan Ho · Pieter Abbeel -
2018 Poster: Where Do You Think You're Going?: Inferring Beliefs about Dynamics from Behavior »
Sid Reddy · Anca Dragan · Sergey Levine -
2018 Poster: Unsupervised Text Style Transfer using Language Models as Discriminators »
Zichao Yang · Zhiting Hu · Chris Dyer · Eric Xing · Taylor Berg-Kirkpatrick -
2018 Poster: Co-teaching: Robust training of deep neural networks with extremely noisy labels »
Bo Han · Quanming Yao · Xingrui Yu · Gang Niu · Miao Xu · Weihua Hu · Ivor Tsang · Masashi Sugiyama -
2018 Poster: The Importance of Sampling inMeta-Reinforcement Learning »
Bradly Stadie · Ge Yang · Rein Houthooft · Peter Chen · Yan Duan · Yuhuai Wu · Pieter Abbeel · Ilya Sutskever -
2017 : Poster Session »
David Abel · Nicholas Denis · Maria Eckstein · Ronan Fruit · Karan Goel · Joshua Gruenstein · Anna Harutyunyan · Martin Klissarov · Xiangyu Kong · Aviral Kumar · Saurabh Kumar · Miao Liu · Daniel McNamee · Shayegan Omidshafiei · Silviu Pitis · Paulo Rauber · Melrose Roderick · Tianmin Shu · Yizhou Wang · Shangtong Zhang -
2017 : Panel Discussion »
Matt Botvinick · Emma Brunskill · Marcos Campos · Jan Peters · Doina Precup · David Silver · Josh Tenenbaum · Roy Fox -
2017 : Invited Talk 7 »
Trevor Darrell -
2017 : Progress on Deep Reinforcement Learning with Temporal Abstraction (Doina Precup) »
Doina Precup -
2017 : Doina Precup »
Doina Precup -
2017 : Deep Kernel Learning »
Ruslan Salakhutdinov -
2017 : Adaptive Deep Learning for Perception, Action, and Explanation, Trevor Darrell (UC Berkeley) »
Trevor Darrell -
2017 : Spotlights & Poster Session »
David Abel · Nicholas Denis · Maria Eckstein · Ronan Fruit · Karan Goel · Joshua Gruenstein · Anna Harutyunyan · Martin Klissarov · Xiangyu Kong · Aviral Kumar · Saurabh Kumar · Miao Liu · Daniel McNamee · Shayegan Omidshafiei · Silviu Pitis · Paulo Rauber · Melrose Roderick · Tianmin Shu · Yizhou Wang · Shangtong Zhang -
2017 : Meta-Learning Shared Hierarchies (Pieter Abbeel) »
Pieter Abbeel -
2017 : Exhausting the Sim with Domain Randomization and Trying to Exhaust the Real World, Pieter Abbeel, UC Berkeley and Embodied Intelligence »
Pieter Abbeel · Gregory Kahn -
2017 Workshop: Hierarchical Reinforcement Learning »
Andrew G Barto · Doina Precup · Shie Mannor · Tom Schaul · Roy Fox · Carlos Florensa -
2017 Workshop: Workshop on Meta-Learning »
Roberto Calandra · Frank Hutter · Hugo Larochelle · Sergey Levine -
2017 : Panel Discussion »
Felix Hill · Olivier Pietquin · Jack Gallant · Raymond Mooney · Sanja Fidler · Chen Yu · Devi Parikh -
2017 : Poster Session (encompasses coffee break) »
Beidi Chen · Borja Balle · Daniel Lee · iuri frosio · Jitendra Malik · Jan Kautz · Ke Li · Masashi Sugiyama · Miguel A. Carreira-Perpinan · Ramin Raziperchikolaei · Theja Tulabandhula · Yung-Kyun Noh · Adams Wei Yu -
2017 : Dialogue systems and RL: interconnecting language, vision and rewards »
Olivier Pietquin -
2017 : How to stop worrying and learn to love Nearest Neighbors »
Alexei Efros -
2017 Workshop: Visually grounded interaction and language »
Florian Strub · Harm de Vries · Abhishek Das · Satwik Kottur · Stefan Lee · Mateusz Malinowski · Olivier Pietquin · Devi Parikh · Dhruv Batra · Aaron Courville · Jeremie Mary -
2017 Symposium: Deep Reinforcement Learning »
Pieter Abbeel · Yan Duan · David Silver · Satinder Singh · Junhyuk Oh · Rein Houthooft -
2017 Oral: Deep Sets »
Manzil Zaheer · Satwik Kottur · Siamak Ravanbakhsh · Barnabas Poczos · Ruslan Salakhutdinov · Alexander Smola -
2017 Poster: Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets »
Karol Hausman · Yevgen Chebotar · Stefan Schaal · Gaurav Sukhatme · Joseph Lim -
2017 Poster: EX2: Exploration with Exemplar Models for Deep Reinforcement Learning »
Justin Fu · John Co-Reyes · Sergey Levine -
2017 Poster: #Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning »
Haoran Tang · Rein Houthooft · Davis Foote · Adam Stooke · OpenAI Xi Chen · Yan Duan · John Schulman · Filip DeTurck · Pieter Abbeel -
2017 Poster: Is the Bellman residual a bad proxy? »
Matthieu Geist · Bilal Piot · Olivier Pietquin -
2017 Poster: Dynamic-Depth Context Tree Weighting »
Joao V Messias · Shimon Whiteson -
2017 Poster: Deep Sets »
Manzil Zaheer · Satwik Kottur · Siamak Ravanbakhsh · Barnabas Poczos · Ruslan Salakhutdinov · Alexander Smola -
2017 Poster: Structured Generative Adversarial Networks »
Zhijie Deng · Hao Zhang · Xiaodan Liang · Luona Yang · Shizhen Xu · Jun Zhu · Eric Xing -
2017 Poster: Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments »
Ryan Lowe · YI WU · Aviv Tamar · Jean Harb · OpenAI Pieter Abbeel · Igor Mordatch -
2017 Poster: Good Semi-supervised Learning That Requires a Bad GAN »
Zihang Dai · Zhilin Yang · Fan Yang · William Cohen · Ruslan Salakhutdinov -
2017 Poster: Inverse Reward Design »
Dylan Hadfield-Menell · Smitha Milli · Pieter Abbeel · Stuart J Russell · Anca Dragan -
2017 Spotlight: EX2: Exploration with Exemplar Models for Deep Reinforcement Learning »
Justin Fu · John Co-Reyes · Sergey Levine -
2017 Oral: Inverse Reward Design »
Dylan Hadfield-Menell · Smitha Milli · Pieter Abbeel · Stuart J Russell · Anca Dragan -
2017 Invited Talk: Deep Learning for Robotics »
Pieter Abbeel -
2017 Demonstration: Deep Robotic Learning using Visual Imagination and Meta-Learning »
Chelsea Finn · Frederik Ebert · Tianhe Yu · Annie Xie · Sudeep Dasari · Pieter Abbeel · Sergey Levine -
2017 Poster: Positive-Unlabeled Learning with Non-Negative Risk Estimator »
Ryuichi Kiryo · Gang Niu · Marthinus C du Plessis · Masashi Sugiyama -
2017 Poster: Learning from Complementary Labels »
Takashi Ishida · Gang Niu · Weihua Hu · Masashi Sugiyama -
2017 Poster: Modulating early visual processing by language »
Harm de Vries · Florian Strub · Jeremie Mary · Hugo Larochelle · Olivier Pietquin · Aaron Courville -
2017 Spotlight: Modulating early visual processing by language »
Harm de Vries · Florian Strub · Jeremie Mary · Hugo Larochelle · Olivier Pietquin · Aaron Courville -
2017 Oral: Positive-Unlabeled Learning with Non-Negative Risk Estimator »
Ryuichi Kiryo · Gang Niu · Marthinus C du Plessis · Masashi Sugiyama -
2017 Poster: Toward Multimodal Image-to-Image Translation »
Jun-Yan Zhu · Richard Zhang · Deepak Pathak · Trevor Darrell · Alexei Efros · Oliver Wang · Eli Shechtman -
2017 Poster: One-Shot Imitation Learning »
Yan Duan · Marcin Andrychowicz · Bradly Stadie · OpenAI Jonathan Ho · Jonas Schneider · Ilya Sutskever · Pieter Abbeel · Wojciech Zaremba -
2017 Poster: Expectation Propagation for t-Exponential Family Using q-Algebra »
Futoshi Futami · Issei Sato · Masashi Sugiyama -
2017 Poster: Generative Local Metric Learning for Kernel Regression »
Yung-Kyun Noh · Masashi Sugiyama · Kee-Eung Kim · Frank Park · Daniel Lee -
2017 Poster: Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning »
Shixiang (Shane) Gu · Timothy Lillicrap · Richard Turner · Zoubin Ghahramani · Bernhard Schölkopf · Sergey Levine -
2016 : What makes ImageNet good for Transfer Learning? »
Jacob MY Huh · Pulkit Agrawal · Alexei Efros -
2016 : Anima Anandkumar »
Anima Anandkumar -
2016 Workshop: Deep Learning for Action and Interaction »
Chelsea Finn · Raia Hadsell · David Held · Sergey Levine · Percy Liang -
2016 Workshop: Learning with Tensors: Why Now and How? »
Anima Anandkumar · Rong Ge · Yan Liu · Maximilian Nickel · Qi (Rose) Yu -
2016 : Invited Talk: Learning Adaptive Driving Models from Large-scale Video Datasets (Fisher Yu, Huazhe Xu, Dequan Wang, and Trevor Darrell, Berkeley) »
Trevor Darrell -
2016 : Pieter Abbeel (University of California, Berkeley) »
Pieter Abbeel -
2016 : Sergey Levine (University of California, Berkeley) »
Sergey Levine -
2016 : Invited Talk: Olivier Pietquin »
Olivier Pietquin -
2016 : Eric Xing »
Eric Xing -
2016 : Learning to Communicate with Deep Multi−Agent Reinforcement Learning »
Shimon Whiteson -
2016 : Invited Talk: Safe Reinforcement Learning for Robotics (Pieter Abbeel, UC Berkeley and OpenAI) »
Pieter Abbeel -
2016 Workshop: Nonconvex Optimization for Machine Learning: Theory and Practice »
Hossein Mobahi · Anima Anandkumar · Percy Liang · Stefanie Jegelka · Anna Choromanska -
2016 Workshop: Deep Reinforcement Learning »
David Silver · Satinder Singh · Pieter Abbeel · Peter Chen -
2016 Workshop: The Future of Interactive Machine Learning »
Kory Mathewson @korymath · Kaushik Subramanian · Mark Ho · Robert Loftin · Joseph L Austerweil · Anna Harutyunyan · Doina Precup · Layla El Asri · Matthew Gombolay · Jerry Zhu · Sonia Chernova · Charles Isbell · Patrick M Pilarski · Weng-Keen Wong · Manuela Veloso · Julie A Shah · Matthew Taylor · Brenna Argall · Michael Littman -
2016 Workshop: Machine Learning for Intelligent Transportation Systems »
Li Erran Li · Trevor Darrell -
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: Backprop KF: Learning Discriminative Deterministic State Estimators »
Tuomas Haarnoja · Anurag Ajay · Sergey Levine · Pieter Abbeel -
2016 Poster: Learning to Poke by Poking: Experiential Learning of Intuitive Physics »
Pulkit Agrawal · Ashvin Nair · Pieter Abbeel · Jitendra Malik · Sergey Levine -
2016 Oral: Learning to Poke by Poking: Experiential Learning of Intuitive Physics »
Pulkit Agrawal · Ashvin Nair · Pieter Abbeel · Jitendra Malik · Sergey Levine -
2016 Poster: Combinatorial Energy Learning for Image Segmentation »
Jeremy Maitin-Shepard · Viren Jain · Michal Januszewski · Peter Li · Pieter Abbeel -
2016 Poster: InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets »
Xi Chen · Peter Chen · Yan Duan · Rein Houthooft · John Schulman · Ilya Sutskever · Pieter Abbeel -
2016 Poster: VIME: Variational Information Maximizing Exploration »
Rein Houthooft · Xi Chen · Peter Chen · Yan Duan · John Schulman · Filip De Turck · Pieter Abbeel -
2016 Poster: Learning HMMs with Nonparametric Emissions via Spectral Decompositions of Continuous Matrices »
Kirthevasan Kandasamy · Maruan Al-Shedivat · Eric Xing -
2016 Poster: Value Iteration Networks »
Aviv Tamar · Sergey Levine · Pieter Abbeel · YI WU · Garrett Thomas -
2016 Oral: Value Iteration Networks »
Aviv Tamar · Sergey Levine · Pieter Abbeel · YI WU · Garrett Thomas -
2016 Poster: Learning to Communicate with Deep Multi-Agent Reinforcement Learning »
Jakob Foerster · Yannis Assael · Nando de Freitas · Shimon Whiteson -
2016 Poster: Cooperative Inverse Reinforcement Learning »
Dylan Hadfield-Menell · Stuart J Russell · Pieter Abbeel · Anca Dragan -
2016 Poster: Stochastic Variational Deep Kernel Learning »
Andrew Wilson · Zhiting Hu · Russ Salakhutdinov · Eric Xing -
2016 Poster: Online and Differentially-Private Tensor Decomposition »
Yining Wang · Anima Anandkumar -
2016 Poster: Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning »
Gang Niu · Marthinus Christoffel du Plessis · Tomoya Sakai · Yao Ma · Masashi Sugiyama -
2016 Tutorial: Deep Reinforcement Learning Through Policy Optimization »
Pieter Abbeel · John Schulman -
2015 : Intro and Adapting Deep Networks Across Domains, Modalities, and Tasks »
Trevor Darrell -
2015 : Opening and Overview »
Anima Anandkumar -
2015 Workshop: Non-convex Optimization for Machine Learning: Theory and Practice »
Anima Anandkumar · Niranjan Uma Naresh · Kamalika Chaudhuri · Percy Liang · Sewoong Oh -
2015 : Deep Robotic Learning »
Sergey Levine -
2015 : Machine Learning For Conversational Systems »
Larry Heck · Li Deng · Olivier Pietquin · Tomas Mikolov -
2015 Workshop: Deep Reinforcement Learning »
Pieter Abbeel · John Schulman · Satinder Singh · David Silver -
2015 Workshop: Nonparametric Methods for Large Scale Representation Learning »
Andrew G Wilson · Alexander Smola · Eric Xing -
2015 Poster: Copeland Dueling Bandits »
Masrour Zoghi · Zohar Karnin · Shimon Whiteson · Maarten de Rijke -
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: Gradient Estimation Using Stochastic Computation Graphs »
John Schulman · Nicolas Heess · Theophane Weber · Pieter Abbeel -
2015 Poster: The Human Kernel »
Andrew Wilson · Christoph Dann · Chris Lucas · Eric Xing -
2015 Poster: Data Generation as Sequential Decision Making »
Philip Bachman · Doina Precup -
2015 Spotlight: The Human Kernel »
Andrew Wilson · Christoph Dann · Chris Lucas · Eric Xing -
2015 Spotlight: Data Generation as Sequential Decision Making »
Philip Bachman · Doina Precup -
2015 Poster: Basis refinement strategies for linear value function approximation in MDPs »
Gheorghe Comanici · Doina Precup · Prakash Panangaden -
2014 Workshop: Novel Trends and Applications in Reinforcement Learning »
Csaba Szepesvari · Marc Deisenroth · Sergey Levine · Pedro Ortega · Brian Ziebart · Emma Brunskill · Naftali Tishby · Gerhard Neumann · Daniel Lee · Sridhar Mahadevan · Pieter Abbeel · David Silver · Vicenç Gómez -
2014 Workshop: Modern Nonparametrics 3: Automating the Learning Pipeline »
Eric Xing · Mladen Kolar · Arthur Gretton · Samory Kpotufe · Han Liu · Zoltán Szabó · Alan Yuille · Andrew G Wilson · Ryan Tibshirani · Sasha Rakhlin · Damian Kozbur · Bharath Sriperumbudur · David Lopez-Paz · Kirthevasan Kandasamy · Francesco Orabona · Andreas Damianou · Wacha Bounliphone · Yanshuai Cao · Arijit Das · Yingzhen Yang · Giulia DeSalvo · Dmitry Storcheus · Roberto Valerio -
2014 Workshop: From Bad Models to Good Policies (Sequential Decision Making under Uncertainty) »
Odalric-Ambrym Maillard · Timothy A Mann · Shie Mannor · Jeremie Mary · Laurent Orseau · Thomas Dietterich · Ronald Ortner · Peter Grünwald · Joelle Pineau · Raphael Fonteneau · Georgios Theocharous · Esteban D Arcaute · Christos Dimitrakakis · Nan Jiang · Doina Precup · Pierre-Luc Bacon · Marek Petrik · Aviv Tamar -
2014 Workshop: Modern Machine Learning and Natural Language Processing »
Ankur P Parikh · Avneesh Saluja · Chris Dyer · Eric Xing -
2014 Poster: Analysis of Variational Bayesian Latent Dirichlet Allocation: Weaker Sparsity Than MAP »
Shinichi Nakajima · Issei Sato · Masashi Sugiyama · Kazuho Watanabe · Hiroko Kobayashi -
2014 Poster: Multi-Step Stochastic ADMM in High Dimensions: Applications to Sparse Optimization and Matrix Decomposition »
Hanie Sedghi · Anima Anandkumar · Edmond A Jonckheere -
2014 Poster: Multitask learning meets tensor factorization: task imputation via convex optimization »
Kishan Wimalawarne · Masashi Sugiyama · Ryota Tomioka -
2014 Poster: On Model Parallelization and Scheduling Strategies for Distributed Machine Learning »
Seunghak Lee · Jin Kyu Kim · Xun Zheng · Qirong Ho · Garth Gibson · Eric Xing -
2014 Poster: Difference of Convex Functions Programming for Reinforcement Learning »
Bilal Piot · Matthieu Geist · Olivier Pietquin -
2014 Poster: Non-convex Robust PCA »
Praneeth Netrapalli · Niranjan Uma Naresh · Sujay Sanghavi · Animashree Anandkumar · Prateek Jain -
2014 Poster: Optimizing Energy Production Using Policy Search and Predictive State Representations »
Yuri Grinberg · Doina Precup · Michel Gendreau -
2014 Poster: Learning Neural Network Policies with Guided Policy Search under Unknown Dynamics »
Sergey Levine · Pieter Abbeel -
2014 Poster: Analysis of Learning from Positive and Unlabeled Data »
Marthinus C du Plessis · Gang Niu · Masashi Sugiyama -
2014 Poster: Learning with Pseudo-Ensembles »
Philip Bachman · Ouais Alsharif · Doina Precup -
2014 Spotlight: Difference of Convex Functions Programming for Reinforcement Learning »
Bilal Piot · Matthieu Geist · Olivier Pietquin -
2014 Spotlight: Optimizing Energy Production Using Policy Search and Predictive State Representations »
Yuri Grinberg · Doina Precup · Michel Gendreau -
2014 Spotlight: Learning Neural Network Policies with Guided Policy Search under Unknown Dynamics »
Sergey Levine · Pieter Abbeel -
2014 Spotlight: Non-convex Robust PCA »
Praneeth Netrapalli · Niranjan Uma Naresh · Sujay Sanghavi · Animashree Anandkumar · Prateek Jain -
2014 Poster: Do Convnets Learn Correspondence? »
Jonathan L Long · Ning Zhang · Trevor Darrell -
2014 Poster: LSDA: Large Scale Detection through Adaptation »
Judy Hoffman · Sergio Guadarrama · Eric Tzeng · Ronghang Hu · Jeff Donahue · Ross Girshick · Trevor Darrell · Kate Saenko -
2014 Poster: Dependent nonparametric trees for dynamic hierarchical clustering »
Kumar Avinava Dubey · Qirong Ho · Sinead Williamson · Eric Xing -
2014 Poster: Weakly-supervised Discovery of Visual Pattern Configurations »
Hyun Oh Song · Yong Jae Lee · Stefanie Jegelka · Trevor Darrell -
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 Poster: Variational Policy Search via Trajectory Optimization »
Sergey Levine · Vladlen Koltun -
2013 Poster: Parametric Task Learning »
Ichiro Takeuchi · Tatsuya Hongo · Masashi Sugiyama · Shinichi Nakajima -
2013 Poster: Visual Concept Learning: Combining Machine Vision and Bayesian Generalization on Concept Hierarchies »
Yangqing Jia · Joshua T Abbott · Joseph L Austerweil · Tom Griffiths · Trevor Darrell -
2013 Poster: Learning from Limited Demonstrations »
Beomjoon Kim · Amir-massoud Farahmand · Joelle Pineau · Doina Precup -
2013 Poster: Bellman Error Based Feature Generation using Random Projections on Sparse Spaces »
Mahdi Milani Fard · Yuri Grinberg · Amir-massoud Farahmand · Joelle Pineau · Doina Precup -
2013 Spotlight: Learning from Limited Demonstrations »
Beomjoon Kim · Amir-massoud Farahmand · Joelle Pineau · Doina Precup -
2013 Poster: More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server »
Qirong Ho · James Cipar · Henggang Cui · Seunghak Lee · Jin Kyu Kim · Phillip B. Gibbons · Garth Gibson · Greg Ganger · Eric Xing -
2013 Oral: More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server »
Qirong Ho · James Cipar · Henggang Cui · Seunghak Lee · Jin Kyu Kim · Phillip B. Gibbons · Garth Gibson · Greg Ganger · Eric Xing -
2013 Poster: Variance Reduction for Stochastic Gradient Optimization »
Chong Wang · Xi Chen · Alexander Smola · Eric Xing -
2013 Poster: Global Solver and Its Efficient Approximation for Variational Bayesian Low-rank Subspace Clustering »
Shinichi Nakajima · Akiko Takeda · S. Derin Babacan · Masashi Sugiyama · Ichiro Takeuchi -
2013 Poster: Restricting exchangeable nonparametric distributions »
Sinead Williamson · Steven MacEachern · Eric Xing -
2013 Spotlight: Restricting exchangeable nonparametric distributions »
Sinead Williamson · Steven MacEachern · Eric Xing -
2013 Poster: A Scalable Approach to Probabilistic Latent Space Inference of Large-Scale Networks »
Junming Yin · Qirong Ho · Eric Xing -
2013 Poster: When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity »
Anima Anandkumar · Daniel Hsu · Majid Janzamin · Sham M Kakade -
2012 Workshop: Spectral Algorithms for Latent Variable Models »
Ankur P Parikh · Le Song · Eric Xing -
2012 Poster: Inverse Reinforcement Learning through Structured Classification »
Edouard Klein · Matthieu Geist · BILAL PIOT · Olivier Pietquin -
2012 Poster: Value Pursuit Iteration »
Amir-massoud Farahmand · Doina Precup -
2012 Poster: Monte Carlo Methods for Maximum Margin Supervised Topic Models »
Qixia Jiang · Jun Zhu · Maosong Sun · Eric Xing -
2012 Poster: Learning with Recursive Perceptual Representations »
Oriol Vinyals · Yangqing Jia · Li Deng · Trevor Darrell -
2012 Poster: Learning Mixtures of Tree Graphical Models »
Anima Anandkumar · Daniel Hsu · Furong Huang · Sham M Kakade -
2012 Poster: A Spectral Algorithm for Latent Dirichlet Allocation »
Anima Anandkumar · Dean P Foster · Daniel Hsu · Sham M Kakade · Yi-Kai Liu -
2012 Poster: On Triangular versus Edge Representations --- Towards Scalable Modeling of Networks »
Qirong Ho · Junming Yin · Eric Xing -
2012 Poster: Symmetric Correspondence Topic Models for Multilingual Text Analysis »
Kosuke Fukumasu · Koji Eguchi · Eric Xing -
2012 Poster: Near Optimal Chernoff Bounds for Markov Decision Processes »
Teodor Mihai Moldovan · Pieter Abbeel -
2012 Spotlight: A Spectral Algorithm for Latent Dirichlet Allocation »
Anima Anandkumar · Dean P Foster · Daniel Hsu · Sham M Kakade · Yi-Kai Liu -
2012 Spotlight: Symmetric Correspondence Topic Models for Multilingual Text Analysis »
Kosuke Fukumasu · Koji Eguchi · Eric Xing -
2012 Spotlight: Near Optimal Chernoff Bounds for Markov Decision Processes »
Teodor Mihai Moldovan · Pieter Abbeel -
2012 Poster: Timely Object Recognition »
Sergey K Karayev · Tobi Baumgartner · Mario Fritz · Trevor Darrell -
2012 Poster: Latent Graphical Model Selection: Efficient Methods for Locally Tree-like Graphs »
Anima Anandkumar · Ragupathyraj Valluvan -
2012 Poster: On-line Reinforcement Learning Using Incremental Kernel-Based Stochastic Factorization »
Andre S Barreto · Doina Precup · Joelle Pineau -
2012 Poster: Perfect Dimensionality Recovery by Variational Bayesian PCA »
Shinichi Nakajima · Ryota Tomioka · Masashi Sugiyama · S. Derin Babacan -
2012 Poster: Density-Difference Estimation »
Masashi Sugiyama · Takafumi Kanamori · Taiji Suzuki · Marthinus C du Plessis · Song Liu · Ichiro Takeuchi -
2011 Workshop: Integrating Language and Vision »
Raymond Mooney · Trevor Darrell · Kate Saenko -
2011 Poster: Heavy-tailed Distances for Gradient Based Image Descriptors »
Yangqing Jia · Trevor Darrell -
2011 Poster: High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions »
Animashree Anandkumar · Vincent Tan · Alan S Willsky -
2011 Poster: Understanding the Intrinsic Memorability of Images »
Phillip Isola · Devi Parikh · Antonio Torralba · Aude Oliva -
2011 Oral: High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions »
Animashree Anandkumar · Vincent Tan · Alan S Willsky -
2011 Poster: Relative Density-Ratio Estimation for Robust Distribution Comparison »
Makoto Yamada · Taiji Suzuki · Takafumi Kanamori · Hirotaka Hachiya · Masashi Sugiyama -
2011 Poster: Spectral Methods for Learning Multivariate Latent Tree Structure »
Anima Anandkumar · Kamalika Chaudhuri · Daniel Hsu · Sham M Kakade · Le Song · Tong Zhang -
2011 Poster: Target Neighbor Consistent Feature Weighting for Nearest Neighbor Classification »
Ichiro Takeuchi · Masashi Sugiyama -
2011 Poster: Analysis and Improvement of Policy Gradient Estimation »
Tingting Zhao · Hirotaka Hachiya · Gang Niu · Masashi Sugiyama -
2011 Poster: Global Solution of Fully-Observed Variational Bayesian Matrix Factorization is Column-Wise Independent »
Shinichi Nakajima · Masashi Sugiyama · S. Derin Babacan -
2011 Poster: Infinite Latent SVM for Classification and Multi-task Learning »
Jun Zhu · Ning Chen · Eric Xing -
2011 Poster: Kernel Embeddings of Latent Tree Graphical Models »
Le Song · Ankur P Parikh · Eric Xing -
2011 Poster: Large-Scale Category Structure Aware Image Categorization »
Bin Zhao · Li Fei-Fei · Eric Xing -
2011 Poster: Reinforcement Learning using Kernel-Based Stochastic Factorization »
Andre S Barreto · Doina Precup · Joelle Pineau -
2010 Spotlight: On a Connection between Importance Sampling and the Likelihood Ratio Policy Gradient »
Jie Tang · Pieter Abbeel -
2010 Spotlight: Global Analytic Solution for Variational Bayesian Matrix Factorization »
Shinichi Nakajima · Masashi Sugiyama · Ryota Tomioka -
2010 Poster: Feature Construction for Inverse Reinforcement Learning »
Sergey Levine · Zoran Popovic · Vladlen Koltun -
2010 Poster: Large Margin Learning of Upstream Scene Understanding Models »
Jun Zhu · Li-Jia Li · Li Fei-Fei · Eric Xing -
2010 Poster: On a Connection between Importance Sampling and the Likelihood Ratio Policy Gradient »
Jie Tang · Pieter Abbeel -
2010 Poster: Global Analytic Solution for Variational Bayesian Matrix Factorization »
Shinichi Nakajima · Masashi Sugiyama · Ryota Tomioka -
2010 Poster: Factorized Latent Spaces with Structured Sparsity »
Yangqing Jia · Mathieu Salzmann · Trevor Darrell -
2010 Poster: Predictive Subspace Learning for Multi-view Data: a Large Margin Approach »
Ning Chen · Jun Zhu · Eric Xing -
2010 Poster: Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification »
Li-Jia Li · Hao Su · Eric Xing · Li Fei-Fei -
2010 Poster: Adaptive Multi-Task Lasso: with Application to eQTL Detection »
Seunghak Lee · Jun Zhu · Eric Xing -
2010 Poster: Size Matters: Metric Visual Search Constraints from Monocular Metadata »
Mario J Fritz · Kate Saenko · Trevor Darrell -
2009 Poster: Learning to Hash with Binary Reconstructive Embeddings »
Brian Kulis · Trevor Darrell -
2009 Poster: Heterogeneous multitask learning with joint sparsity constraints »
Xiaolin Yang · Seyoung Kim · Eric Xing -
2009 Spotlight: Learning to Hash with Binary Reconstructive Embeddings »
Brian Kulis · Trevor Darrell -
2009 Poster: An Additive Latent Feature Model for Transparent Object Recognition »
Mario J Fritz · Michael J Black · Gary R Bradski · Trevor Darrell -
2009 Poster: Time-Varying Dynamic Bayesian Networks »
Le Song · Mladen Kolar · Eric Xing -
2009 Poster: Filtering Abstract Senses From Image Search Results »
Kate Saenko · Trevor Darrell -
2009 Spotlight: Time-Varying Dynamic Bayesian Networks »
Le Song · Mladen Kolar · Eric Xing -
2009 Oral: An Additive Latent Feature Model for Transparent Object Recognition »
Mario J Fritz · Michael J Black · Gary R Bradski · Trevor Darrell -
2009 Poster: Sparsistent Learning of Varying-coefficient Models with Structural Changes »
Mladen Kolar · Le Song · Eric Xing -
2009 Poster: Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation »
Hamid R Maei · Csaba Szepesvari · Shalabh Batnaghar · Doina Precup · David Silver · Richard Sutton -
2009 Spotlight: Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation »
Hamid R Maei · Csaba Szepesvari · Shalabh Batnaghar · Doina Precup · David Silver · Richard Sutton -
2009 Spotlight: Sparsistent Learning of Varying-coefficient Models with Structural Changes »
Mladen Kolar · Le Song · Eric Xing -
2008 Workshop: Stochastic Models of Behaviour »
Aldo A Faisal · Marta Gonzalez -
2008 Workshop: Analyzing Graphs: Theory and Applications »
Edo M Airoldi · David Blei · Jake M Hofman · Tony Jebara · Eric Xing -
2008 Poster: Mixed Membership Stochastic Blockmodels »
Edo M Airoldi · David Blei · Stephen E Fienberg · Eric Xing -
2008 Spotlight: Mixed Membership Stochastic Blockmodels »
Edo M Airoldi · David Blei · Stephen E Fienberg · Eric Xing -
2008 Poster: Partially Observed Maximum Entropy Discrimination Markov Networks »
Jun Zhu · Eric Xing · Bo Zhang -
2008 Poster: Unsupervised Learning of Visual Sense Models for Polysemous Words »
Kate Saenko · Trevor Darrell -
2008 Poster: Efficient Direct Density Ratio Estimation for Non-stationarity Adaptation and Outlier Detection »
Takafumi Kanamori · Shohei Hido · Masashi Sugiyama -
2008 Poster: Bounding Performance Loss in Approximate MDP Homomorphisms »
Doina Precup · Jonathan Taylor Taylor · Prakash Panangaden -
2008 Spotlight: Unsupervised Learning of Visual Sense Models for Polysemous Words »
Kate Saenko · Trevor Darrell -
2007 Workshop: Statistical Network Models »
Kevin Murphy · Lise Getoor · Eric Xing · Raphael Gottardo -
2007 Spotlight: Hierarchical Apprenticeship Learning with Application to Quadruped Locomotion »
J. Zico Kolter · Pieter Abbeel · Andrew Y Ng -
2007 Poster: Hierarchical Apprenticeship Learning with Application to Quadruped Locomotion »
J. Zico Kolter · Pieter Abbeel · Andrew Y Ng -
2007 Poster: Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation »
Masashi Sugiyama · Shinichi Nakajima · Hisashi Kashima · Paul von Buenau · Motoaki Kawanabe -
2007 Poster: Multi-Task Learning via Conic Programming »
Tsuyoshi Kato · Hisashi Kashima · Masashi Sugiyama · Kiyoshi Asai -
2007 Poster: HM-BiTAM: Bilingual Topic Exploration, Word Alignment, and Translation »
Bing Zhao · Eric Xing -
2006 Workshop: Learning when test and training inputs have different distributions »
Joaquin Quiñonero-Candela · Masashi Sugiyama · Anton Schwaighofer · Neil D Lawrence -
2006 Poster: A Hidden Markov Dirichlet Process Model for Genetic Recombination in Open Ancestral Space »
KyungAh Sohn · Eric Xing -
2006 Poster: Max-margin classification of incomplete data »
Gal Chechik · Geremy Heitz · Gal Elidan · Pieter Abbeel · Daphne Koller -
2006 Poster: Approximate Correspondences in High Dimensions »
Kristen Grauman · Trevor Darrell -
2006 Spotlight: Approximate Correspondences in High Dimensions »
Kristen Grauman · Trevor Darrell -
2006 Talk: A Hidden Markov Dirichlet Process Model for Genetic Recombination in Open Ancestral Space »
KyungAh Sohn · Eric Xing -
2006 Spotlight: Max-margin classification of incomplete data »
Gal Chechik · Geremy Heitz · Gal Elidan · Pieter Abbeel · Daphne Koller -
2006 Poster: Mixture Regression for Covariate Shift »
Amos Storkey · Masashi Sugiyama -
2006 Poster: An Application of Reinforcement Learning to Aerobatic Helicopter Flight »
Pieter Abbeel · Adam P Coates · Andrew Y Ng · Morgan Quigley -
2006 Talk: An Application of Reinforcement Learning to Aerobatic Helicopter Flight »
Pieter Abbeel · Adam P Coates · Andrew Y Ng · Morgan Quigley