Guess Who Rated This Movie: Identifying Users Through Subspace Clustering. - Amy Zhang 001; Nadia Fawaz; Stratis Ioannidis; Andrea Montanari (UAI, 2012)

How to hide the elephant- or the donkey- in the room: Practical privacy against statistical inference for large data. - Salman Salamatian; Amy Zhang 001; Flávio du Pin Calmon; Sandilya Bhamidipati; Nadia Fawaz; Branislav Kveton; Pedro Oliveira; Nina Taft (GlobalSIP, 2013)

Managing Your Private and Public Data: Bringing Down Inference Attacks Against Your Privacy. - Salman Salamatian; Amy Zhang 001; Flávio du Pin Calmon; Sandilya Bhamidipati; Nadia Fawaz; Branislav Kveton; Pedro Oliveira; Nina Taft (IEEE J. Sel. Top. Signal Process., 2015)

PriView: Personalized Media Consumption Meets Privacy against Inference Attacks. - Sandilya Bhamidipati; Nadia Fawaz; Branislav Kveton; Amy Zhang 001 (IEEE Softw., 2015)

Feedback Neural Network for Weakly Supervised Geo-Semantic Segmentation. - Xianming Liu; Amy Zhang 001; Tobias Tiecke; Andreas Gros; Thomas S. Huang (CoRR, 2016)

Building Detection from Satellite Images on a Global Scale. - Amy Zhang 001; Xianming Liu; Andreas Gros; Tobias Tiecke (CoRR, 2017)

Mapping the world population one building at a time. - Tobias G. Tiecke; Xianming Liu; Amy Zhang 001; Andreas Gros; Nan Li; Gregory Yetman; Talip Kilic; Siobhan Murray; Brian Blankespoor; Espen B. Prydz; Hai-Anh H. Dang (CoRR, 2017)

Decoupling Dynamics and Reward for Transfer Learning. - Amy Zhang 001; Harsh Satija; Joelle Pineau (ICLR (Workshop), 2018)

Composable Planning with Attributes. - Amy Zhang 001; Sainbayar Sukhbaatar; Adam Lerer; Arthur Szlam; Rob Fergus (ICML, 2018)

A Dissection of Overfitting and Generalization in Continuous Reinforcement Learning. - Amy Zhang 001; Nicolas Ballas; Joelle Pineau (CoRR, 2018)

Natural Environment Benchmarks for Reinforcement Learning. - Amy Zhang 001; Yuxin Wu; Joelle Pineau (CoRR, 2018)

Learning Causal State Representations of Partially Observable Environments. - Amy Zhang 001; Zachary C. Lipton; Luis Pineda; Kamyar Azizzadenesheli; Anima Anandkumar; Laurent Itti; Joelle Pineau; Tommaso Furlanello (CoRR, 2019)

Invariant Causal Prediction for Block MDPs. - Amy Zhang 001; Clare Lyle; Shagun Sodhani; Angelos Filos; Marta Kwiatkowska; Joelle Pineau; Yarin Gal; Doina Precup (ICML, 2020)

Plan2Vec: Unsupervised Representation Learning by Latent Plans. - Ge Yang 003; Amy Zhang 001; Ari S. Morcos; Joelle Pineau; Pieter Abbeel; Roberto Calandra (L4DC, 2020)

Stable Policy Optimization via Off-Policy Divergence Regularization. - Ahmed Touati; Amy Zhang 001; Joelle Pineau; Pascal Vincent (UAI, 2020)

Out-of-Distribution Generalization via Risk Extrapolation (REx). - David Krueger 001; Ethan Caballero; Jörn-Henrik Jacobsen; Amy Zhang 001; Jonathan Binas; Rémi Le Priol; Aaron C. Courville (CoRR, 2020)

Multi-Task Reinforcement Learning as a Hidden-Parameter Block MDP. - Amy Zhang 001; Shagun Sodhani; Khimya Khetarpal; Joelle Pineau (CoRR, 2020)

Intervention Design for Effective Sim2Real Transfer. - Melissa Mozifian; Amy Zhang 001; Joelle Pineau; David Meger (CoRR, 2020)

Improving Sample Efficiency in Model-Free Reinforcement Learning from Images. - Denis Yarats; Amy Zhang 001; Ilya Kostrikov; Brandon Amos; Joelle Pineau; Rob Fergus (AAAI, 2021)

Learning Invariant Representations for Reinforcement Learning without Reconstruction. - Amy Zhang 001; Rowan Thomas McAllister; Roberto Calandra; Yarin Gal; Sergey Levine (ICLR, 2021)

Learning Robust State Abstractions for Hidden-Parameter Block MDPs. - Amy Zhang 001; Shagun Sodhani; Khimya Khetarpal; Joelle Pineau (ICLR, 2021)

Out-of-Distribution Generalization via Risk Extrapolation (REx). - David Krueger 001; Ethan Caballero; Jörn-Henrik Jacobsen; Amy Zhang 001; Jonathan Binas; Dinghuai Zhang; Rémi Le Priol; Aaron C. Courville (ICML, 2021)

Multi-Task Reinforcement Learning with Context-based Representations. - Shagun Sodhani; Amy Zhang 001; Joelle Pineau (ICML, 2021)

Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability. - Dibya Ghosh; Jad Rahme; Aviral Kumar; Amy Zhang 001; Ryan P. Adams; Sergey Levine (NeurIPS, 2021)

Model-Invariant State Abstractions for Model-Based Reinforcement Learning. - Manan Tomar; Amy Zhang 001; Roberto Calandra; Matthew E. Taylor; Joelle Pineau (CoRR, 2021)

MBRL-Lib: A Modular Library for Model-based Reinforcement Learning. - Luis Pineda; Brandon Amos; Amy Zhang 001; Nathan O. Lambert; Roberto Calandra (CoRR, 2021)

Provably Efficient Representation Learning in Low-rank Markov Decision Processes. - Weitong Zhang; Jiafan He; Dongruo Zhou; Amy Zhang 001; Quanquan Gu (CoRR, 2021)

A Survey of Generalisation in Deep Reinforcement Learning. - Robert Kirk; Amy Zhang 001; Edward Grefenstette; Tim Rocktäschel (CoRR, 2021)

Denoised MDPs: Learning World Models Better Than the World Itself. - Tongzhou Wang 001; Simon S. Du; Antonio Torralba 001; Phillip Isola; Amy Zhang 001; Yuandong Tian (ICML, 2022)

Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learning. - Philippe Hansen-Estruch; Amy Zhang 001; Ashvin Nair; Patrick Yin; Sergey Levine (ICML, 2022)

Robust Policy Learning over Multiple Uncertainty Sets. - Annie Xie; Shagun Sodhani; Chelsea Finn; Joelle Pineau; Amy Zhang 001 (ICML, 2022)

Online Decision Transformer. - Qinqing Zheng; Amy Zhang 001; Aditya Grover (ICML, 2022)

Block Contextual MDPs for Continual Learning. - Shagun Sodhani; Franziska Meier; Joelle Pineau; Amy Zhang 001 (L4DC, 2022)

AutoCAT: Reinforcement Learning for Automated Exploration of Cache Timing-Channel Attacks. - Mulong Luo; Wenjie Xiong 001; Geunbae Lee; Yueying Li; Xiaomeng Yang; Amy Zhang 001; Yuandong Tian; Hsien-Hsin S. Lee; G. Edward Suh (CoRR, 2022)

LAD: Language Augmented Diffusion for Reinforcement Learning. - Edwin Zhang; Yujie Lu; William Wang 001; Amy Zhang 001 (CoRR, 2022)

Contrastive Distillation Is a Sample-Efficient Self-Supervised Loss Policy for Transfer Learning. - Chris Lengerich; Gabriel Synnaeve; Amy Zhang 001; Hugh Leather; Kurt Shuster 001; François Charton; Charysse Redwood (CoRR, 2022)

AutoCAT: Reinforcement Learning for Automated Exploration of Cache-Timing Attacks. - Mulong Luo; Wenjie Xiong 001; Geunbae Lee; Yueying Li; Xiaomeng Yang; Amy Zhang 001; Yuandong Tian; Hsien-Hsin S. Lee; G. Edward Suh (HPCA, 2023)

Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement. - Michael Chang 003; Alyssa L. Dayan; Franziska Meier; Thomas L. Griffiths 001; Sergey Levine; Amy Zhang 001 (ICLR, 2023)

VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training. - Yecheng Jason Ma; Shagun Sodhani; Dinesh Jayaraman; Osbert Bastani; Vikash Kumar; Amy Zhang 001 (ICLR, 2023)

BC-IRL: Learning Generalizable Reward Functions from Demonstrations. - Andrew Szot; Amy Zhang 001; Dhruv Batra; Zsolt Kira; Franziska Meier (ICLR, 2023)

Latent State Marginalization as a Low-cost Approach for Improving Exploration. - Dinghuai Zhang; Aaron C. Courville; Yoshua Bengio; Qinqing Zheng; Amy Zhang 001; Ricky T. Q. Chen (ICLR, 2023)

Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learning. - Tongzhou Wang 001; Antonio Torralba 001; Phillip Isola; Amy Zhang 001 (ICML, 2023)

LIV: Language-Image Representations and Rewards for Robotic Control. - Yecheng Jason Ma; Vikash Kumar; Amy Zhang 001; Osbert Bastani; Dinesh Jayaraman (ICML, 2023)

f-Policy Gradients: A General Framework for Goal-Conditioned RL using f-Divergences. - Siddhant Agarwal; Ishan Durugkar; Peter Stone 001; Amy Zhang 001 (NeurIPS, 2023)

Accelerating Exploration with Unlabeled Prior Data. - Qiyang Li; Jason Zhang; Dibya Ghosh; Amy Zhang 001; Sergey Levine (NeurIPS, 2023)

Provably Efficient Offline Goal-Conditioned Reinforcement Learning with General Function Approximation and Single-Policy Concentrability. - Hanlin Zhu; Amy Zhang 001 (NeurIPS, 2023)

Provably efficient representation selection in Low-rank Markov Decision Processes: from online to offline RL. - Weitong Zhang; Jiafan He; Dongruo Zhou; Amy Zhang 001; Quanquan Gu (UAI, 2023)

Imitation from Arbitrary Experience: A Dual Unification of Reinforcement and Imitation Learning Methods. - Harshit Sikchi; Amy Zhang 001; Scott Niekum (CoRR, 2023)

Neural Constraint Satisfaction: Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement. - Michael Chang 003; Alyssa L. Dayan; Franziska Meier; Thomas L. Griffiths 001; Sergey Levine; Amy Zhang 001 (CoRR, 2023)

Sequence Modeling is a Robust Contender for Offline Reinforcement Learning. - Prajjwal Bhargava; Rohan Chitnis; Alborz Geramifard; Shagun Sodhani; Amy Zhang 001 (CoRR, 2023)

LIV: Language-Image Representations and Rewards for Robotic Control. - Yecheng Jason Ma; William Liang; Vaidehi Som; Vikash Kumar; Amy Zhang 001; Osbert Bastani; Dinesh Jayaraman (CoRR, 2023)

Generalization Across Observation Shifts in Reinforcement Learning. - Anuj Mahajan; Amy Zhang 001 (CoRR, 2023)

Structure in Reinforcement Learning: A Survey and Open Problems. - Aditya Mohan; Amy Zhang 001; Marius Lindauer (CoRR, 2023)

A Survey of Zero-shot Generalisation in Deep Reinforcement Learning. - Robert Kirk; Amy Zhang 001; Edward Grefenstette; Tim Rocktäschel (J. Artif. Intell. Res., 2023)

Improving Generalization with Approximate Factored Value Functions. - Shagun Sodhani; Sergey Levine; Amy Zhang 001 (Trans. Mach. Learn. Res., 2023)

Learning Representations for Pixel-based Control: What Matters and Why? - Manan Tomar; Utkarsh A. Mishra; Amy Zhang 001; Matthew E. Taylor (Trans. Mach. Learn. Res., 2023)

A Model-Based Solution to the Offline Multi-Agent Reinforcement Learning Coordination Problem. - Paul Barde; Jakob N. Foerster; Derek Nowrouzezahrai; Amy Zhang 001 (AAMAS, 2024)

A Dual Approach to Imitation Learning from Observations with Offline Datasets. - Harshit Sikchi; Caleb Chuck; Amy Zhang 001; Scott Niekum (CoRL, 2024)

Towards Robust Offline Reinforcement Learning under Diverse Data Corruption. - Rui Yang 010; Han Zhong 001; Jiawei Xu; Amy Zhang 001; Chongjie Zhang; Lei Han 001; Tong Zhang 001 (ICLR, 2024)

When should we prefer Decision Transformers for Offline Reinforcement Learning? - Prajjwal Bhargava; Rohan Chitnis; Alborz Geramifard; Shagun Sodhani; Amy Zhang 001 (ICLR, 2024)

Motif: Intrinsic Motivation from Artificial Intelligence Feedback. - Martin Klissarov; Pierluca D'Oro; Shagun Sodhani; Roberta Raileanu; Pierre-Luc Bacon; Pascal Vincent; Amy Zhang 001; Mikael Henaff (ICLR, 2024)

Score Models for Offline Goal-Conditioned Reinforcement Learning. - Harshit Sikchi; Rohan Chitnis; Ahmed Touati; Alborz Geramifard; Amy Zhang 001; Scott Niekum (ICLR, 2024)

Dual RL: Unification and New Methods for Reinforcement and Imitation Learning. - Harshit Sikchi; Qinqing Zheng; Amy Zhang 001; Scott Niekum (ICLR, 2024)

Language Control Diffusion: Efficiently Scaling through Space, Time, and Tasks. - Edwin Zhang; Yujie Lu; Shinda Huang; William Yang Wang; Amy Zhang 001 (ICLR, 2024)

Zero-Shot Reinforcement Learning via Function Encoders. - Tyler Ingebrand; Amy Zhang 001; Ufuk Topcu (ICML, 2024)

An investigation of time reversal symmetry in reinforcement learning. - Brett Barkley; Amy Zhang 001; David Fridovich-Keil (L4DC, 2024)

Efficient Reinforcement Learning by Discovering Neural Pathways. - Samin Yeasar Arnob; Riyasat Ohib; Sergey M. Plis; Amy Zhang 001; Alessandro Sordoni; Doina Precup (NeurIPS, 2024)

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers. - Jake Grigsby; Justin Sasek; Samyak Parajuli; Daniel Adebi; Amy Zhang 001; Yuke Zhu (NeurIPS, 2024)

Diffusion-DICE: In-Sample Diffusion Guidance for Offline Reinforcement Learning. - Liyuan Mao; Haoran Xu 003; Xianyuan Zhan; Weinan Zhang 001; Amy Zhang 001 (NeurIPS, 2024)

SkiLD: Unsupervised Skill Discovery Guided by Factor Interactions. - Zizhao Wang; Jiaheng Hu; Caleb Chuck; Stephen Chen; Roberto Martín-Martín; Amy Zhang 001; Scott Niekum; Peter Stone 001 (NeurIPS, 2024)

Multistep Inverse Is Not All You Need. - Alexander Levine 001; Peter Stone 001; Amy Zhang 001 (RLJ, 2024)

Learning Action-based Representations Using Invariance. - Max Rudolph; Caleb Chuck; Kevin Black; Misha Lvovsky; Scott Niekum; Amy Zhang 001 (RLJ, 2024)

Diffusion World Model. - Zihan Ding; Amy Zhang 001; Yuandong Tian; Qinqing Zheng (CoRR, 2024)

Automated Discovery of Functional Actual Causes in Complex Environments. - Caleb Chuck; Sankaran Vaidyanathan; Stephen Giguere 001; Amy Zhang 001; David Jensen; Scott Niekum (CoRR, 2024)

Robot Air Hockey: A Manipulation Testbed for Robot Learning with Reinforcement Learning. - Caleb Chuck; Carl Qi; Michael J. Munje; Shuozhe Li; Max Rudolph; Chang Shi; Siddhant Agarwal; Harshit Sikchi; Abhinav Peri; Sarthak Dayal; Evan Kuo; Kavan Mehta; et al. (CoRR, 2024)

Unified Auto-Encoding with Masked Diffusion. - Philippe Hansen-Estruch; Sriram Vishwanath; Amy Zhang 001; Manan Tomar (CoRR, 2024)

CAT: Caution Aware Transfer in Reinforcement Learning via Distributional Risk. - Mohamad Fares El Hajj Chehade; Amrit Singh Bedi; Amy Zhang 001; Hao Zhu (CoRR, 2024)

Online Intrinsic Rewards for Decision Making Agents from Large Language Model Feedback. - Qinqing Zheng; Mikael Henaff; Amy Zhang 001; Aditya Grover; Brandon Amos (CoRR, 2024)

Proto Successor Measure: Representing the Space of All Possible Solutions of Reinforcement Learning. - Siddhant Agarwal; Harshit Sikchi; Peter Stone 001; Amy Zhang 001 (CoRR, 2024)

RL Zero: Zero-Shot Language to Behaviors without any Supervision. - Harshit Sikchi; Siddhant Agarwal; Pranaya Jajoo; Samyak Parajuli; Caleb Chuck; Max Rudolph; Peter Stone 001; Amy Zhang 001; Scott Niekum (CoRR, 2024)

Structure in Deep Reinforcement Learning: A Survey and Open Problems. - Aditya Mohan; Amy Zhang 001; Marius Lindauer (J. Artif. Intell. Res., 2024)

StitchLLM: Serving LLMs, One Block at a Time. - Bodun Hu; Shuozhe Li; Saurabh Agarwal; Myungjin Lee; Akshay Jajoo; Jiamin Li 002; Le Xu; Geon-Woo Kim; Donghyun Kim 002; Hong Xu 001; Amy Zhang 001; Aditya Akella (ACL (1), 2025)

Learning a Fast Mixing Exogenous Block MDP using a Single Trajectory. - Alexander Levine 001; Peter Stone 001; Amy Zhang 001 (ICLR, 2025)

Null Counterfactual Factor Interactions for Goal-Conditioned Reinforcement Learning. - Caleb Chuck; Fan Feng; Carl Qi; Chang Shi; Siddhant Agarwal; Amy Zhang 001; Scott Niekum (ICLR, 2025)

Towards General-Purpose Model-Free Reinforcement Learning. - Scott Fujimoto; Pierluca D'Oro; Amy Zhang 001; Yuandong Tian; Michael Rabbat (ICLR, 2025)

MaestroMotif: Skill Design from Artificial Intelligence Feedback. - Martin Klissarov; Mikael Henaff; Roberta Raileanu; Shagun Sodhani; Pascal Vincent; Amy Zhang 001; Pierre-Luc Bacon; Doina Precup; Marlos C. Machado; Pierluca D'Oro (ICLR, 2025)

EC-Diffuser: Multi-Object Manipulation via Entity-Centric Behavior Generation. - Carl Qi; Dan Haramati; Tal Daniel; Aviv Tamar; Amy Zhang 001 (ICLR, 2025)

An Optimal Discriminator Weighted Imitation Perspective for Reinforcement Learning. - Haoran Xu 003; Shuozhe Li; Harshit Sikchi; Scott Niekum; Amy Zhang 001 (ICLR, 2025)

Augmented Conditioning Is Enough For Effective Training Image Generation. - Jiahui Chen; Amy Zhang 001; Adriana Romero-Soriano (CoRR, 2025)

Reevaluating Policy Gradient Methods for Imperfect-Information Games. - Max Rudolph; Nathan Lichtle; Sobhan Mohammadpour; Alexandre M. Bayen; J. Zico Kolter; Amy Zhang 001; Gabriele Farina; Eugene Vinitsky; Samuel Sokota (CoRR, 2025)

CREStE: Scalable Mapless Navigation with Internet Scale Priors and Counterfactual Guidance. - Arthur Zhang; Harshit Sikchi; Amy Zhang 001; Joydeep Biswas (CoRR, 2025)

Offline Action-Free Learning of Ex-BMDPs by Comparing Diverse Datasets. - Alexander Levine 001; Peter Stone 001; Amy Zhang 001 (CoRR, 2025)

Information-Theoretic Reward Decomposition for Generalizable RLHF. - Liyuan Mao; Haoran Xu 003; Amy Zhang 001; Weinan Zhang 001; Chenjia Bai (CoRR, 2025)

Fast Adaptation with Behavioral Foundation Models. - Harshit Sikchi; Andrea Tirinzoni; Ahmed Touati; Yingchen Xu; Anssi Kanervisto; Scott Niekum; Amy Zhang 001; Alessandro Lazaric; Matteo Pirotta (CoRR, 2025)

Guiding Diffusion with Deep Geometric Moments: Balancing Fidelity and Variation. - Sangmin Jung; Utkarsh Nath; Yezhou Yang; Giulia Pedrielli; Joydeep Biswas; Amy Zhang 001; Hassan Ghasemzadeh 001; Pavan K. Turaga (CoRR, 2025)

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks. - Viraj Joshi; Zifan Xu; Bo Liu 042; Peter Stone 001; Amy Zhang 001 (CoRR, 2025)