Timezone: »
A core capability of intelligent systems is the ability to quickly learn new tasks by drawing on prior experience. Gradient (or optimization) based meta-learning has recently emerged as an effective approach for few-shot learning. In this formulation, meta-parameters are learned in the outer loop, while task-specific models are learned in the inner-loop, by using only a small amount of data from the current task. A key challenge in scaling these approaches is the need to differentiate through the inner loop learning process, which can impose considerable computational and memory burdens. By drawing upon implicit differentiation, we develop the implicit MAML algorithm, which depends only on the solution to the inner level optimization and not the path taken by the inner loop optimizer. This effectively decouples the meta-gradient computation from the choice of inner loop optimizer. As a result, our approach is agnostic to the choice of inner loop optimizer and can gracefully handle many gradient steps without vanishing gradients or memory constraints. Theoretically, we prove that implicit MAML can compute accurate meta-gradients with a memory footprint that is, up to small constant factors, no more than that which is required to compute a single inner loop gradient and at no overall increase in the total computational cost. Experimentally, we show that these benefits of implicit MAML translate into empirical gains on few-shot image recognition benchmarks.
Author Information
Aravind Rajeswaran (University of Washington)
Chelsea Finn (Stanford University)
Sham Kakade (University of Washington)
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
More from the Same Authors
-
2021 Spotlight: Robust Predictable Control »
Ben Eysenbach · Russ Salakhutdinov · Sergey Levine -
2021 Spotlight: Offline Reinforcement Learning as One Big Sequence Modeling Problem »
Michael Janner · Qiyang Li · Sergey Levine -
2021 Spotlight: Pragmatic Image Compression for Human-in-the-Loop Decision-Making »
Sid Reddy · Anca Dragan · Sergey Levine -
2021 : Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain Datasets »
Frederik Ebert · Yanlai Yang · Karl Schmeckpeper · Bernadette Bucher · Kostas Daniilidis · Chelsea Finn · Sergey Levine -
2021 : Hybrid Imitative Planning with Geometric and Predictive Costs in Offroad Environments »
Dhruv Shah · Daniel Shin · Nick Rhinehart · Ali Agha · David D Fan · Sergey Levine -
2021 : Extending the WILDS Benchmark for Unsupervised Adaptation »
Shiori Sagawa · Pang Wei Koh · Tony Lee · Irena Gao · Sang Michael Xie · Kendrick Shen · Ananya Kumar · Weihua Hu · Michihiro Yasunaga · Henrik Marklund · Sara Beery · Ian Stavness · Jure Leskovec · Kate Saenko · Tatsunori Hashimoto · Sergey Levine · Chelsea Finn · Percy Liang -
2021 : Test Time Robustification of Deep Models via Adaptation and Augmentation »
Marvin Zhang · Sergey Levine · Chelsea Finn -
2021 : Value Function Spaces: Skill-Centric State Abstractions for Long-Horizon Reasoning »
Dhruv Shah · Ted Xiao · Alexander Toshev · Sergey Levine · brian ichter -
2021 : Data Sharing without Rewards in Multi-Task Offline Reinforcement Learning »
Tianhe Yu · Aviral Kumar · Yevgen Chebotar · Chelsea Finn · Sergey Levine · Karol Hausman -
2021 : Should I Run Offline Reinforcement Learning or Behavioral Cloning? »
Aviral Kumar · Joey Hong · Anikait Singh · 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 : CIC: Contrastive Intrinsic Control for Unsupervised Skill Discovery »
Misha Laskin · Hao Liu · Xue Bin Peng · Denis Yarats · Aravind Rajeswaran · Pieter Abbeel -
2021 : Offline Reinforcement Learning with In-sample Q-Learning »
Ilya Kostrikov · Ashvin Nair · Sergey Levine -
2021 : C-Planning: An Automatic Curriculum for Learning Goal-Reaching Tasks »
Tianjun Zhang · Ben Eysenbach · Russ Salakhutdinov · Sergey Levine · Joseph Gonzalez -
2021 : The Information Geometry of Unsupervised Reinforcement Learning »
Ben Eysenbach · Russ Salakhutdinov · Sergey Levine -
2021 : Mismatched No More: Joint Model-Policy Optimization for Model-Based RL »
Ben Eysenbach · Alexander Khazatsky · Sergey Levine · Russ Salakhutdinov -
2021 : Offline Meta-Reinforcement Learning with Online Self-Supervision »
Vitchyr Pong · Ashvin Nair · Laura Smith · Catherine Huang · Sergey Levine -
2021 : Hybrid Imitative Planning with Geometric and Predictive Costs in Offroad Environments »
Daniel Shin · Dhruv Shah · Ali Agha · Nicholas Rhinehart · Sergey Levine -
2021 : CoMPS: Continual Meta Policy Search »
Glen Berseth · Zhiwei Zhang · Grace Zhang · Chelsea Finn · Sergey Levine -
2021 : Behavioral Priors and Dynamics Models: Improving Performance and Domain Transfer in Offline RL »
Catherine Cang · Aravind Rajeswaran · Pieter Abbeel · Misha Laskin -
2021 : Offline Reinforcement Learning with Implicit Q-Learning »
Ilya Kostrikov · Ashvin Nair · Sergey Levine -
2021 : TRAIL: Near-Optimal Imitation Learning with Suboptimal Data »
Mengjiao (Sherry) Yang · Sergey Levine · Ofir Nachum -
2022 : You Only Live Once: Single-Life Reinforcement Learning »
Annie Chen · Archit Sharma · Sergey Levine · Chelsea Finn -
2022 : Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement »
Michael Chang · Alyssa L Dayan · Franziska Meier · Tom Griffiths · Sergey Levine · Amy Zhang -
2022 : Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement »
Michael Chang · Alyssa L Dayan · Franziska Meier · Tom Griffiths · Sergey Levine · Amy Zhang -
2022 : Offline Q-learning on Diverse Multi-Task Data Both Scales And Generalizes »
Aviral Kumar · Rishabh Agarwal · XINYANG GENG · George Tucker · Sergey Levine -
2022 : Pre-Training for Robots: Leveraging Diverse Multitask Data via Offline Reinforcement Learning »
Aviral Kumar · Anikait Singh · 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 : Skill Acquisition by Instruction Augmentation on Offline Datasets »
Ted Xiao · Harris Chan · Pierre Sermanet · Ayzaan Wahid · Anthony Brohan · Karol Hausman · Sergey Levine · Jonathan Tompson -
2022 : PnP-Nav: Plug-and-Play Policies for Generalizable Visual Navigation Across Robots »
Dhruv Shah · Ajay Sridhar · Arjun Bhorkar · Noriaki Hirose · Sergey Levine -
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 : Real World Offline Reinforcement Learning with Realistic Data Source »
Gaoyue Zhou · Liyiming Ke · Siddhartha Srinivasa · Abhinav Gupta · Aravind Rajeswaran · Vikash Kumar -
2022 : Bitrate-Constrained DRO: Beyond Worst Case Robustness To Unknown Group Shifts »
Amrith Setlur · Don Dennis · Benjamin Eysenbach · Aditi Raghunathan · Chelsea Finn · Virginia Smith · Sergey Levine -
2022 : Confidence-Conditioned Value Functions for Offline Reinforcement Learning »
Joey Hong · Aviral Kumar · Sergey Levine -
2022 : Efficient Deep Reinforcement Learning Requires Regulating Statistical Overfitting »
Qiyang Li · Aviral Kumar · Ilya Kostrikov · Sergey Levine -
2022 : Contrastive Example-Based Control »
Kyle Hatch · Sarthak J Shetty · Benjamin Eysenbach · Tianhe Yu · Rafael Rafailov · Russ Salakhutdinov · Sergey Levine · Chelsea Finn -
2022 : Offline Reinforcement Learning for Customizable Visual Navigation »
Dhruv Shah · Arjun Bhorkar · Hrishit Leen · Ilya Kostrikov · Nicholas Rhinehart · Sergey Levine -
2022 : A Connection between One-Step Regularization and Critic Regularization in Reinforcement Learning »
Benjamin Eysenbach · Matthieu Geist · Sergey Levine · Russ Salakhutdinov -
2022 : Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement »
Michael Chang · Alyssa L Dayan · Franziska Meier · Tom Griffiths · Sergey Levine · Amy Zhang -
2022 : Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement »
Michael Chang · Alyssa L Dayan · Franziska Meier · Tom Griffiths · Sergey Levine · Amy Zhang -
2022 : Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement »
Michael Chang · Alyssa L Dayan · Franziska Meier · Tom Griffiths · Sergey Levine · Amy Zhang -
2022 : Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement »
Michael Chang · Alyssa L Dayan · Franziska Meier · Tom Griffiths · Sergey Levine · Amy Zhang -
2022 : Confidence-Conditioned Value Functions for Offline Reinforcement Learning »
Joey Hong · Aviral Kumar · Sergey Levine -
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 : 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 : 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 : 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 : Real World Offline Reinforcement Learning with Realistic Data Source »
Gaoyue Zhou · Liyiming Ke · Siddhartha Srinivasa · Abhinav Gupta · Aravind Rajeswaran · Vikash Kumar -
2023 Poster: ReDS: Offline RL With Heteroskedastic Datasets via Support Constraints »
Anikait Singh · Aviral Kumar · Quan Vuong · Yevgen Chebotar · Sergey Levine -
2023 Poster: Ignorance is Bliss: Robust Control via Information Gating »
Manan Tomar · Riashat Islam · Matthew Taylor · Sergey Levine · Philip Bachman -
2023 Poster: Learning to Influence Human Behavior with Offline Reinforcement Learning »
Joey Hong · Sergey Levine · Anca Dragan -
2023 Poster: Offline Goal-Conditioned RL with Latent States as Actions »
Seohong Park · Dibya Ghosh · Benjamin Eysenbach · Sergey Levine -
2023 Poster: Grounded Decoding: Guiding Text Generation with Grounded Models for Robot Control »
Wenlong Huang · Fei Xia · Dhruv Shah · Danny Driess · Andy Zeng · Yao Lu · Pete Florence · Igor Mordatch · Sergey Levine · Karol Hausman · brian ichter -
2023 Poster: Accelerating Exploration with Unlabeled Prior Data »
Qiyang Li · Jason Zhang · Dibya Ghosh · Amy Zhang · Sergey Levine -
2023 Poster: Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning »
Mitsuhiko Nakamoto · Yuexiang Zhai · Anikait Singh · Max Sobol Mark · Yi Ma · Chelsea Finn · Aviral Kumar · Sergey Levine -
2022 : Offline Q-learning on Diverse Multi-Task Data Both Scales And Generalizes »
Aviral Kumar · Rishabh Agarwal · XINYANG GENG · George Tucker · Sergey Levine -
2022 Spotlight: Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime »
Difan Zou · Jingfeng Wu · Vladimir Braverman · Quanquan Gu · Sham Kakade -
2022 : Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement »
Michael Chang · Alyssa L Dayan · Franziska Meier · Tom Griffiths · Sergey Levine · Amy Zhang -
2022 Poster: MEMO: Test Time Robustness via Adaptation and Augmentation »
Marvin Zhang · Sergey Levine · Chelsea Finn -
2022 Poster: First Contact: Unsupervised Human-Machine Co-Adaptation via Mutual Information Maximization »
Siddharth Reddy · Sergey Levine · Anca Dragan -
2022 Poster: DASCO: Dual-Generator Adversarial Support Constrained Offline Reinforcement Learning »
Quan Vuong · Aviral Kumar · Sergey Levine · Yevgen Chebotar -
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: Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexity »
Abhishek Gupta · Aldo Pacchiano · Yuexiang Zhai · Sham Kakade · Sergey Levine -
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: Object Representations as Fixed Points: Training Iterative Refinement Algorithms with Implicit Differentiation »
Michael Chang · Tom Griffiths · Sergey Levine -
2022 Poster: Data-Driven Offline Decision-Making via Invariant Representation Learning »
Han Qi · Yi Su · Aviral Kumar · Sergey Levine -
2022 Poster: Contrastive Learning as Goal-Conditioned Reinforcement Learning »
Benjamin Eysenbach · Tianjun Zhang · Sergey Levine · Russ Salakhutdinov -
2022 Poster: Imitating Past Successes can be Very Suboptimal »
Benjamin Eysenbach · Soumith Udatha · Russ Salakhutdinov · Sergey Levine -
2021 : Retrospective Panel »
Sergey Levine · Nando de Freitas · Emma Brunskill · Finale Doshi-Velez · Nan Jiang · Rishabh Agarwal -
2021 : Invited Speaker Panel »
Sham Kakade · Minmin Chen · Philip Thomas · Angela Schoellig · Barbara Engelhardt · Doina Precup · George Tucker -
2021 : Q&A for Sham Kakade »
Sham Kakade -
2021 : Generalization theory in Offline RL »
Sham Kakade -
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 : Data-Driven Offline Optimization for Architecting Hardware Accelerators »
Aviral Kumar · Amir Yazdanbakhsh · Milad Hashemi · Kevin Swersky · Sergey Levine -
2021 : Sergey Levine Talk Q&A »
Sergey Levine -
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 : 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 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 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: The Benefits of Implicit Regularization from SGD in Least Squares Problems »
Difan Zou · Jingfeng Wu · Vladimir Braverman · Quanquan Gu · Dean Foster · Sham Kakade -
2021 Poster: Robust and differentially private mean estimation »
Xiyang Liu · Weihao Kong · Sham Kakade · Sewoong Oh -
2021 Poster: Robust Predictable Control »
Ben Eysenbach · Russ Salakhutdinov · Sergey Levine -
2021 Poster: Which Mutual-Information Representation Learning Objectives are Sufficient for Control? »
Kate Rakelly · Abhishek Gupta · Carlos Florensa · Sergey Levine -
2021 Poster: An Exponential Lower Bound for Linearly Realizable MDP with Constant Suboptimality Gap »
Yuanhao Wang · Ruosong Wang · Sham Kakade -
2021 Poster: COMBO: Conservative Offline Model-Based Policy Optimization »
Tianhe Yu · Aviral Kumar · Rafael Rafailov · Aravind Rajeswaran · Sergey Levine · Chelsea Finn -
2021 Poster: Outcome-Driven Reinforcement Learning via Variational Inference »
Tim G. J. Rudner · Vitchyr Pong · Rowan McAllister · Yarin Gal · Sergey Levine -
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: Going Beyond Linear RL: Sample Efficient Neural Function Approximation »
Baihe Huang · Kaixuan Huang · Sham Kakade · Jason Lee · Qi Lei · Runzhe Wang · Jiaqi Yang -
2021 Poster: LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes »
Aditya Kusupati · Matthew Wallingford · Vivek Ramanujan · Raghav Somani · Jae Sung Park · Krishna Pillutla · Prateek Jain · Sham Kakade · Ali Farhadi -
2021 Poster: Bayesian Adaptation for Covariate Shift »
Aurick Zhou · Sergey Levine -
2021 Poster: Gone Fishing: Neural Active Learning with Fisher Embeddings »
Jordan Ash · Surbhi Goel · Akshay Krishnamurthy · Sham Kakade -
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: 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: Reinforcement Learning with Latent Flow »
Wenling Shang · Xiaofei Wang · Aravind Srinivas · Aravind Rajeswaran · Yang Gao · Pieter Abbeel · Misha Laskin -
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: Optimal Gradient-based Algorithms for Non-concave Bandit Optimization »
Baihe Huang · Kaixuan Huang · Sham Kakade · Jason Lee · Qi Lei · Runzhe Wang · Jiaqi Yang -
2021 Poster: Autonomous Reinforcement Learning via Subgoal Curricula »
Archit Sharma · Abhishek Gupta · Sergey Levine · Karol Hausman · Chelsea Finn -
2021 Poster: Adaptive Risk Minimization: Learning to Adapt to Domain Shift »
Marvin Zhang · Henrik Marklund · Nikita Dhawan · Abhishek Gupta · Sergey Levine · Chelsea Finn -
2021 Oral: An Exponential Lower Bound for Linearly Realizable MDP with Constant Suboptimality Gap »
Yuanhao Wang · Ruosong Wang · Sham Kakade -
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 »
Emma Brunskill · Nan Jiang · Nando de Freitas · Finale Doshi-Velez · Sergey Levine · John Langford · Lihong Li · George Tucker · Rishabh Agarwal · Aviral Kumar -
2020 : Contributed Talk: MaxEnt RL and Robust Control »
Benjamin Eysenbach · Sergey Levine -
2020 Workshop: Object Representations for Learning and Reasoning »
William Agnew · Rim Assouel · Michael Chang · Antonia Creswell · Eliza Kosoy · Aravind Rajeswaran · Sjoerd van Steenkiste -
2020 Poster: Model Inversion Networks for Model-Based Optimization »
Aviral Kumar · Sergey Levine -
2020 Tutorial: (Track3) Policy Optimization in Reinforcement Learning Q&A »
Sham M Kakade · Martha White · Nicolas Le Roux -
2020 Poster: Continual Learning of Control Primitives : Skill Discovery via Reset-Games »
Kelvin Xu · Siddharth Verma · Chelsea Finn · Sergey Levine -
2020 Poster: Gradient Surgery for Multi-Task Learning »
Tianhe Yu · Saurabh Kumar · Abhishek Gupta · Sergey Levine · Karol Hausman · Chelsea Finn -
2020 Poster: Robust Meta-learning for Mixed Linear Regression with Small Batches »
Weihao Kong · Raghav Somani · Sham Kakade · Sewoong Oh -
2020 Poster: Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement »
Benjamin Eysenbach · XINYANG GENG · Sergey Levine · Russ Salakhutdinov -
2020 Poster: Is Long Horizon RL More Difficult Than Short Horizon RL? »
Ruosong Wang · Simon Du · Lin Yang · Sham Kakade -
2020 Poster: Conservative Q-Learning for Offline Reinforcement Learning »
Aviral Kumar · Aurick Zhou · George Tucker · Sergey Levine -
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: Gamma-Models: Generative Temporal Difference Learning for Infinite-Horizon Prediction »
Michael Janner · Igor Mordatch · Sergey Levine -
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: FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs »
Alekh Agarwal · Sham Kakade · Akshay Krishnamurthy · Wen Sun -
2020 Poster: PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning »
Alekh Agarwal · Mikael Henaff · Sham Kakade · Wen Sun -
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: Sample-Efficient Reinforcement Learning of Undercomplete POMDPs »
Chi Jin · Sham Kakade · Akshay Krishnamurthy · Qinghua Liu -
2020 Poster: Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model »
Alex X. Lee · Anusha Nagabandi · Pieter Abbeel · Sergey Levine -
2020 Spotlight: Sample-Efficient Reinforcement Learning of Undercomplete POMDPs »
Chi Jin · Sham Kakade · Akshay Krishnamurthy · Qinghua Liu -
2020 Oral: FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs »
Alekh Agarwal · Sham Kakade · Akshay Krishnamurthy · Wen Sun -
2020 Poster: MOReL: Model-Based Offline Reinforcement Learning »
Rahul Kidambi · Aravind Rajeswaran · Praneeth Netrapalli · Thorsten Joachims -
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: Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity »
Kaiqing Zhang · Sham Kakade · Tamer Basar · Lin Yang -
2020 Poster: Information Theoretic Regret Bounds for Online Nonlinear Control »
Sham Kakade · Akshay Krishnamurthy · Kendall Lowrey · Motoya Ohnishi · Wen Sun -
2020 Spotlight: Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity »
Kaiqing Zhang · Sham Kakade · Tamer Basar · Lin Yang -
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 Tutorial: (Track3) Policy Optimization in Reinforcement Learning »
Sham M Kakade · Martha White · Nicolas Le Roux -
2020 Tutorial: (Track3) Offline Reinforcement Learning: From Algorithm Design to Practical Applications »
Sergey Levine · Aviral Kumar -
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 Presentations »
Rahul Mehta · Andrew Lampinen · Binghong Chen · Sergio Pascual-Diaz · Jordi Grau-Moya · Aldo Faisal · Jonathan Tompson · Yiren Lu · Khimya Khetarpal · Martin Klissarov · Pierre-Luc Bacon · Doina Precup · Thanard Kurutach · Aviv Tamar · Pieter Abbeel · Jinke He · Maximilian Igl · Shimon Whiteson · Wendelin Boehmer · Raphaël Marinier · Olivier Pietquin · Karol Hausman · Sergey Levine · Chelsea Finn · Tianhe Yu · Lisa Lee · Benjamin Eysenbach · Emilio Parisotto · Eric Xing · Ruslan Salakhutdinov · Hongyu Ren · Anima Anandkumar · Deepak Pathak · Christopher Lu · Trevor Darrell · Alexei Efros · Phillip Isola · Feng Liu · Bo Han · Gang Niu · Masashi Sugiyama · Saurabh Kumar · Janith Petangoda · Johan Ferret · James McClelland · Kara Liu · Animesh Garg · Robert Lange -
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 : The Provable Effectiveness of Policy Gradient Methods in Reinforcement Learning »
Sham Kakade -
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 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 : 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: Wasserstein Dependency Measure for Representation Learning »
Sherjil Ozair · Corey Lynch · Yoshua Bengio · Aaron van den Oord · Sergey Levine · Pierre Sermanet -
2019 Poster: Planning with Goal-Conditioned Policies »
Soroush Nasiriany · Vitchyr Pong · Steven Lin · Sergey Levine -
2019 Poster: The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure For Least Squares »
Rong Ge · Sham Kakade · Rahul Kidambi · Praneeth Netrapalli -
2019 Poster: Search on the Replay Buffer: Bridging Planning and Reinforcement Learning »
Benjamin Eysenbach · Russ Salakhutdinov · Sergey Levine -
2019 Poster: MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies »
Xue Bin Peng · Michael Chang · Grace Zhang · Pieter Abbeel · Sergey Levine -
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 Poster: Compositional Plan Vectors »
Coline Devin · Daniel Geng · Pieter Abbeel · Trevor Darrell · Sergey Levine -
2019 Spotlight: Unsupervised Curricula for Visual Meta-Reinforcement Learning »
Allan Jabri · Kyle Hsu · Abhishek Gupta · Benjamin Eysenbach · Sergey Levine · Chelsea Finn -
2019 Poster: Causal Confusion in Imitation Learning »
Pim de Haan · Dinesh Jayaraman · Sergey Levine -
2019 Poster: When to Trust Your Model: Model-Based Policy Optimization »
Michael Janner · Justin Fu · Marvin Zhang · Sergey Levine -
2019 Poster: Guided Meta-Policy Search »
Russell Mendonca · Abhishek Gupta · Rosen Kralev · Pieter Abbeel · Sergey Levine · Chelsea Finn -
2019 Spotlight: Guided Meta-Policy Search »
Russell Mendonca · Abhishek Gupta · Rosen Kralev · Pieter Abbeel · Sergey Levine · Chelsea Finn -
2019 Oral: Causal Confusion in Imitation Learning »
Pim de Haan · Dinesh Jayaraman · Sergey Levine -
2018 : Meta-Learning to Follow Instructions, Examples, and Demonstrations »
Sergey Levine -
2018 : TBA 2 »
Sergey Levine -
2018 : Learning to Learn from Imperfect Demonstrations »
Ge Yang · Chelsea Finn -
2018 : Control as Inference and Soft Deep RL (Sergey Levine) »
Sergey Levine -
2018 : Chelsea Finn (UCBerkeley / Google Brain): Learning Generalizable Behavior through Unsupervised Interaction »
Chelsea Finn -
2018 : TBC 9 »
Sergey Levine -
2018 : Talk 4: Chelsea Finn - An agent that can do many things
(by modeling the world) »
Chelsea Finn -
2018 : Invited Speaker #1 Chelsea Finn »
Chelsea Finn -
2018 Poster: Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models »
Kurtland Chua · Roberto Calandra · Rowan McAllister · Sergey Levine -
2018 Poster: A Smoother Way to Train Structured Prediction Models »
Krishna Pillutla · Vincent Roulet · Sham Kakade · Zaid Harchaoui -
2018 Spotlight: Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models »
Kurtland Chua · Roberto Calandra · Rowan McAllister · Sergey Levine -
2018 Poster: Probabilistic Model-Agnostic Meta-Learning »
Chelsea Finn · Kelvin Xu · Sergey Levine -
2018 Poster: Meta-Reinforcement Learning of Structured Exploration Strategies »
Abhishek Gupta · Russell Mendonca · YuXuan Liu · Pieter Abbeel · Sergey Levine -
2018 Poster: Visual Reinforcement Learning with Imagined Goals »
Ashvin Nair · Vitchyr Pong · Murtaza Dalal · Shikhar Bahl · Steven Lin · Sergey Levine -
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: Visual Memory for Robust Path Following »
Ashish Kumar · Saurabh Gupta · David Fouhey · Sergey Levine · Jitendra Malik -
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 Oral: Visual Memory for Robust Path Following »
Ashish Kumar · Saurabh Gupta · David Fouhey · Sergey Levine · Jitendra Malik -
2018 Poster: Data-Efficient Hierarchical Reinforcement Learning »
Ofir Nachum · Shixiang (Shane) Gu · Honglak Lee · Sergey Levine -
2018 Poster: Provably Correct Automatic Sub-Differentiation for Qualified Programs »
Sham Kakade · Jason Lee -
2018 Poster: Where Do You Think You're Going?: Inferring Beliefs about Dynamics from Behavior »
Sid Reddy · Anca Dragan · Sergey Levine -
2017 : Model-Agnostic Meta-Learning: Universality, Inductive Bias, and Weak Supervision »
Chelsea Finn -
2017 Workshop: Workshop on Meta-Learning »
Roberto Calandra · Frank Hutter · Hugo Larochelle · Sergey Levine -
2017 Poster: EX2: Exploration with Exemplar Models for Deep Reinforcement Learning »
Justin Fu · John Co-Reyes · Sergey Levine -
2017 Spotlight: EX2: Exploration with Exemplar Models for Deep Reinforcement Learning »
Justin Fu · John Co-Reyes · Sergey Levine -
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: Learning Overcomplete HMMs »
Vatsal Sharan · Sham Kakade · Percy Liang · Gregory Valiant -
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 -
2017 Poster: Towards Generalization and Simplicity in Continuous Control »
Aravind Rajeswaran · Kendall Lowrey · Emanuel Todorov · Sham Kakade -
2016 : Chelsea Finn (University of California, Berkeley) »
Chelsea Finn -
2016 Workshop: Deep Learning for Action and Interaction »
Chelsea Finn · Raia Hadsell · David Held · Sergey Levine · Percy Liang -
2016 : Deep Visual Foresight for Planning Robot Motion »
Chelsea Finn -
2016 : Sergey Levine (University of California, Berkeley) »
Sergey Levine -
2016 : Chelsea Finn »
Chelsea Finn -
2016 Poster: Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent »
Chi Jin · Sham Kakade · Praneeth Netrapalli -
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: Unsupervised Learning for Physical Interaction through Video Prediction »
Chelsea Finn · Ian Goodfellow · Sergey Levine -
2015 : Deep Robotic Learning »
Sergey Levine -
2015 Poster: Convergence Rates of Active Learning for Maximum Likelihood Estimation »
Kamalika Chaudhuri · Sham Kakade · Praneeth Netrapalli · Sujay Sanghavi -
2015 Poster: Super-Resolution Off the Grid »
Qingqing Huang · Sham Kakade -
2015 Spotlight: Super-Resolution Off the Grid »
Qingqing Huang · Sham Kakade -
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 Poster: Learning Neural Network Policies with Guided Policy Search under Unknown Dynamics »
Sergey Levine · Pieter Abbeel -
2014 Spotlight: Learning Neural Network Policies with Guided Policy Search under Unknown Dynamics »
Sergey Levine · Pieter Abbeel -
2013 Poster: Variational Policy Search via Trajectory Optimization »
Sergey Levine · Vladlen Koltun -
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 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: Identifiability and Unmixing of Latent Parse Trees »
Percy Liang · Sham M Kakade · Daniel Hsu -
2012 Spotlight: A Spectral Algorithm for Latent Dirichlet Allocation »
Anima Anandkumar · Dean P Foster · Daniel Hsu · Sham M Kakade · Yi-Kai Liu -
2011 Poster: Stochastic convex optimization with bandit feedback »
Alekh Agarwal · Dean P Foster · Daniel Hsu · Sham M Kakade · Sasha Rakhlin -
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: Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression »
Sham M Kakade · Adam Kalai · Varun Kanade · Ohad Shamir -
2010 Spotlight: Learning from Logged Implicit Exploration Data »
Alex Strehl · Lihong Li · John Langford · Sham M Kakade -
2010 Poster: Feature Construction for Inverse Reinforcement Learning »
Sergey Levine · Zoran Popovic · Vladlen Koltun -
2010 Poster: Learning from Logged Implicit Exploration Data »
Alexander L Strehl · John Langford · Lihong Li · Sham M Kakade -
2009 Poster: Multi-Label Prediction via Compressed Sensing »
Daniel Hsu · Sham M Kakade · John Langford · Tong Zhang -
2009 Oral: Multi-Label Prediction via Compressed Sensing »
Daniel Hsu · Sham M Kakade · John Langford · Tong Zhang -
2008 Poster: Mind the Duality Gap: Logarithmic regret algorithms for online optimization »
Shai Shalev-Shwartz · Sham M Kakade -
2008 Poster: On the Generalization Ability of Online Strongly Convex Programming Algorithms »
Sham M Kakade · Ambuj Tewari -
2008 Spotlight: On the Generalization Ability of Online Strongly Convex Programming Algorithms »
Sham M Kakade · Ambuj Tewari -
2008 Spotlight: Mind the Duality Gap: Logarithmic regret algorithms for online optimization »
Shai Shalev-Shwartz · Sham M Kakade -
2008 Poster: On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization »
Sham M Kakade · Karthik Sridharan · Ambuj Tewari -
2007 Oral: The Price of Bandit Information for Online Optimization »
Varsha Dani · Thomas P Hayes · Sham M Kakade -
2007 Poster: The Price of Bandit Information for Online Optimization »
Varsha Dani · Thomas P Hayes · Sham M Kakade