Amy Zhang

Publication Activity (10 Years)

Years Active: 2012-2024
Publications (10 Years): 67

Top Topics

Decision Process

Reinforcement Learning

Invariant Representations

Partially Observable

Top Venues

Publications

Philippe Hansen-Estruch, Sriram Vishwanath, Amy Zhang, Manan Tomar
Unified Auto-Encoding with Masked Diffusion. CoRR (2024)
Harshit Sikchi, Caleb Chuck, Amy Zhang, Scott Niekum
A Dual Approach to Imitation Learning from Observations with Offline Datasets. CoRR (2024)
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, Anthony Wang, Peter Stone, Amy Zhang, Scott Niekum
Robot Air Hockey: A Manipulation Testbed for Robot Learning with Reinforcement Learning. CoRR (2024)
Max Rudolph, Caleb Chuck, Kevin Black, Misha Lvovsky, Scott Niekum, Amy Zhang
Learning Action-based Representations Using Invariance. CoRR (2024)
Harshit Sikchi, Rohan Chitnis, Ahmed Touati, Alborz Geramifard, Amy Zhang, Scott Niekum
Score Models for Offline Goal-Conditioned Reinforcement Learning. ICLR (2024)
Edwin Zhang, Yujie Lu, Shinda Huang, William Yang Wang, Amy Zhang
Language Control Diffusion: Efficiently Scaling through Space, Time, and Tasks. ICLR (2024)
Harshit Sikchi, Qinqing Zheng, Amy Zhang, Scott Niekum
Dual RL: Unification and New Methods for Reinforcement and Imitation Learning. ICLR (2024)
Martin Klissarov, Pierluca D'Oro, Shagun Sodhani, Roberta Raileanu, Pierre-Luc Bacon, Pascal Vincent, Amy Zhang, Mikael Henaff
Motif: Intrinsic Motivation from Artificial Intelligence Feedback. ICLR (2024)
Prajjwal Bhargava, Rohan Chitnis, Alborz Geramifard, Shagun Sodhani, Amy Zhang
When should we prefer Decision Transformers for Offline Reinforcement Learning? ICLR (2024)
Caleb Chuck, Sankaran Vaidyanathan, Stephen Giguere, Amy Zhang, David Jensen, Scott Niekum
Automated Discovery of Functional Actual Causes in Complex Environments. CoRR (2024)
Manan Tomar, Utkarsh A. Mishra, Amy Zhang, Matthew E. Taylor
Learning Representations for Pixel-based Control: What Matters and Why? Trans. Mach. Learn. Res. 2023 (2023)
Harshit Sikchi, Rohan Chitnis, Ahmed Touati, Alborz Geramifard, Amy Zhang, Scott Niekum
Score Models for Offline Goal-Conditioned Reinforcement Learning. CoRR (2023)
Michael Chang, Alyssa L. Dayan, Franziska Meier, Thomas L. Griffiths, Sergey Levine, Amy Zhang
Neural Constraint Satisfaction: Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement. CoRR (2023)
Martin Klissarov, Pierluca D'Oro, Shagun Sodhani, Roberta Raileanu, Pierre-Luc Bacon, Pascal Vincent, Amy Zhang, Mikael Henaff
Motif: Intrinsic Motivation from Artificial Intelligence Feedback. CoRR (2023)
Qiyang Li, Jason Zhang, Dibya Ghosh, Amy Zhang, Sergey Levine
Accelerating Exploration with Unlabeled Prior Data. CoRR (2023)
Shagun Sodhani, Sergey Levine, Amy Zhang
Improving Generalization with Approximate Factored Value Functions. Trans. Mach. Learn. Res. 2023 (2023)
Michael Chang, Alyssa L. Dayan, Franziska Meier, Thomas L. Griffiths, Sergey Levine, Amy Zhang
Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement. ICLR (2023)
Dinghuai Zhang, Aaron C. Courville, Yoshua Bengio, Qinqing Zheng, Amy Zhang, Ricky T. Q. Chen
Latent State Marginalization as a Low-cost Approach for Improving Exploration. ICLR (2023)
Weitong Zhang, Jiafan He, Dongruo Zhou, Amy Zhang, Quanquan Gu
Provably efficient representation selection in Low-rank Markov Decision Processes: from online to offline RL. UAI (2023)
Yecheng Jason Ma, Shagun Sodhani, Dinesh Jayaraman, Osbert Bastani, Vikash Kumar, Amy Zhang
VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training. ICLR (2023)
Yecheng Jason Ma, William Liang, Vaidehi Som, Vikash Kumar, Amy Zhang, Osbert Bastani, Dinesh Jayaraman
LIV: Language-Image Representations and Rewards for Robotic Control. CoRR (2023)
Yecheng Jason Ma, Vikash Kumar, Amy Zhang, Osbert Bastani, Dinesh Jayaraman
LIV: Language-Image Representations and Rewards for Robotic Control. ICML (2023)
Andrew Szot, Amy Zhang, Dhruv Batra, Zsolt Kira, Franziska Meier
BC-IRL: Learning Generalizable Reward Functions from Demonstrations. CoRR (2023)
Andrew Szot, Amy Zhang, Dhruv Batra, Zsolt Kira, Franziska Meier
BC-IRL: Learning Generalizable Reward Functions from Demonstrations. ICLR (2023)
Qiyang Li, Jason Zhang, Dibya Ghosh, Amy Zhang, Sergey Levine
Accelerating Exploration with Unlabeled Prior Data. NeurIPS (2023)
Annie Xie, Shagun Sodhani, Chelsea Finn, Joelle Pineau, Amy Zhang
Robust Policy Learning over Multiple Uncertainty Sets. CoRR (2022)
Qinqing Zheng, Amy Zhang, Aditya Grover
Online Decision Transformer. ICML (2022)
Philippe Hansen-Estruch, Amy Zhang, Ashvin Nair, Patrick Yin, Sergey Levine
Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learning. ICML (2022)
Annie Xie, Shagun Sodhani, Chelsea Finn, Joelle Pineau, Amy Zhang
Robust Policy Learning over Multiple Uncertainty Sets. ICML (2022)
Shagun Sodhani, Franziska Meier, Joelle Pineau, Amy Zhang
Block Contextual MDPs for Continual Learning. L4DC (2022)
Philippe Hansen-Estruch, Amy Zhang, Ashvin Nair, Patrick Yin, Sergey Levine
Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learning. CoRR (2022)
Dinghuai Zhang, Aaron C. Courville, Yoshua Bengio, Qinqing Zheng, Amy Zhang, Ricky T. Q. Chen
Latent State Marginalization as a Low-cost Approach for Improving Exploration. CoRR (2022)
Yecheng Jason Ma, Shagun Sodhani, Dinesh Jayaraman, Osbert Bastani, Vikash Kumar, Amy Zhang
VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training. CoRR (2022)
Shagun Sodhani, Franziska Meier, Joelle Pineau, Amy Zhang
Block Contextual MDPs for Continual Learning. CoRR (2021)
Dibya Ghosh, Jad Rahme, Aviral Kumar, Amy Zhang, Ryan P. Adams, Sergey Levine
Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability. NeurIPS (2021)
Manan Tomar, Amy Zhang, Roberto Calandra, Matthew E. Taylor, Joelle Pineau
Model-Invariant State Abstractions for Model-Based Reinforcement Learning. CoRR (2021)
David Krueger, Ethan Caballero, Jörn-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Rémi Le Priol, Aaron C. Courville
Out-of-Distribution Generalization via Risk Extrapolation (REx). ICML (2021)
Amy Zhang, Shagun Sodhani, Khimya Khetarpal, Joelle Pineau
Learning Robust State Abstractions for Hidden-Parameter Block MDPs. ICLR (2021)
Denis Yarats, Amy Zhang, Ilya Kostrikov, Brandon Amos, Joelle Pineau, Rob Fergus
Improving Sample Efficiency in Model-Free Reinforcement Learning from Images. AAAI (2021)
Shagun Sodhani, Amy Zhang, Joelle Pineau
Multi-Task Reinforcement Learning with Context-based Representations. CoRR (2021)
Weitong Zhang, Jiafan He, Dongruo Zhou, Amy Zhang, Quanquan Gu
Provably Efficient Representation Learning in Low-rank Markov Decision Processes. CoRR (2021)
Shagun Sodhani, Amy Zhang, Joelle Pineau
Multi-Task Reinforcement Learning with Context-based Representations. ICML (2021)
Dibya Ghosh, Jad Rahme, Aviral Kumar, Amy Zhang, Ryan P. Adams, Sergey Levine
Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability. CoRR (2021)
Amy Zhang, Rowan Thomas McAllister, Roberto Calandra, Yarin Gal, Sergey Levine
Learning Invariant Representations for Reinforcement Learning without Reconstruction. ICLR (2021)
Manan Tomar, Utkarsh A. Mishra, Amy Zhang, Matthew E. Taylor
Learning Representations for Pixel-based Control: What Matters and Why? CoRR (2021)
Luis Pineda, Brandon Amos, Amy Zhang, Nathan O. Lambert, Roberto Calandra
MBRL-Lib: A Modular Library for Model-based Reinforcement Learning. CoRR (2021)
Ahmed Touati, Amy Zhang, Joelle Pineau, Pascal Vincent
Stable Policy Optimization via Off-Policy Divergence Regularization. UAI (2020)
Ge Yang, Amy Zhang, Ari S. Morcos, Joelle Pineau, Pieter Abbeel, Roberto Calandra
Plan2Vec: Unsupervised Representation Learning by Latent Plans. CoRR (2020)
David Krueger, Ethan Caballero, Jörn-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Rémi Le Priol, Aaron C. Courville
Out-of-Distribution Generalization via Risk Extrapolation (REx). CoRR (2020)
Amy Zhang, Shagun Sodhani, Khimya Khetarpal, Joelle Pineau
Multi-Task Reinforcement Learning as a Hidden-Parameter Block MDP. CoRR (2020)
Amy Zhang, Clare Lyle, Shagun Sodhani, Angelos Filos, Marta Kwiatkowska, Joelle Pineau, Yarin Gal, Doina Precup
Invariant Causal Prediction for Block MDPs. CoRR (2020)
Amy Zhang, Rowan McAllister, Roberto Calandra, Yarin Gal, Sergey Levine
Learning Invariant Representations for Reinforcement Learning without Reconstruction. CoRR (2020)
Melissa Mozifian, Amy Zhang, Joelle Pineau, David Meger
Intervention Design for Effective Sim2Real Transfer. CoRR (2020)
Ge Yang, Amy Zhang, Ari S. Morcos, Joelle Pineau, Pieter Abbeel, Roberto Calandra
Plan2Vec: Unsupervised Representation Learning by Latent Plans. L4DC (2020)
Ahmed Touati, Amy Zhang, Joelle Pineau, Pascal Vincent
Stable Policy Optimization via Off-Policy Divergence Regularization. CoRR (2020)
Amy Zhang, Clare Lyle, Shagun Sodhani, Angelos Filos, Marta Kwiatkowska, Joelle Pineau, Yarin Gal, Doina Precup
Invariant Causal Prediction for Block MDPs. ICML (2020)
Denis Yarats, Amy Zhang, Ilya Kostrikov, Brandon Amos, Joelle Pineau, Rob Fergus
Improving Sample Efficiency in Model-Free Reinforcement Learning from Images. CoRR (2019)
Amy Zhang, Zachary C. Lipton, Luis Pineda, Kamyar Azizzadenesheli, Anima Anandkumar, Laurent Itti, Joelle Pineau, Tommaso Furlanello
Learning Causal State Representations of Partially Observable Environments. CoRR (2019)
Amy Zhang, Nicolas Ballas, Joelle Pineau
A Dissection of Overfitting and Generalization in Continuous Reinforcement Learning. CoRR (2018)
Amy Zhang, Harsh Satija, Joelle Pineau
Decoupling Dynamics and Reward for Transfer Learning. ICLR (Workshop) (2018)
Amy Zhang, Adam Lerer, Sainbayar Sukhbaatar, Rob Fergus, Arthur Szlam
Composable Planning with Attributes. CoRR (2018)
Amy Zhang, Yuxin Wu, Joelle Pineau
Natural Environment Benchmarks for Reinforcement Learning. CoRR (2018)
Amy Zhang, Harsh Satija, Joelle Pineau
Decoupling Dynamics and Reward for Transfer Learning. CoRR (2018)
Amy Zhang, Sainbayar Sukhbaatar, Adam Lerer, Arthur Szlam, Rob Fergus
Composable Planning with Attributes. ICML (2018)
Amy Zhang, Xianming Liu, Andreas Gros, Tobias Tiecke
Building Detection from Satellite Images on a Global Scale. CoRR (2017)
Tobias G. Tiecke, Xianming Liu, Amy Zhang, Andreas Gros, Nan Li, Gregory Yetman, Talip Kilic, Siobhan Murray, Brian Blankespoor, Espen B. Prydz, Hai-Anh H. Dang
Mapping the world population one building at a time. CoRR (2017)
Xianming Liu, Amy Zhang, Tobias Tiecke, Andreas Gros, Thomas S. Huang
Feedback Neural Network for Weakly Supervised Geo-Semantic Segmentation. CoRR (2016)
Salman Salamatian, Amy Zhang, Flávio du Pin Calmon, Sandilya Bhamidipati, Nadia Fawaz, Branislav Kveton, Pedro Oliveira, Nina Taft
Managing Your Private and Public Data: Bringing Down Inference Attacks Against Your Privacy. IEEE J. Sel. Top. Signal Process. 9 (7) (2015)
Sandilya Bhamidipati, Nadia Fawaz, Branislav Kveton, Amy Zhang
PriView: Personalized Media Consumption Meets Privacy against Inference Attacks. IEEE Softw. 32 (4) (2015)
Salman Salamatian, Amy Zhang, Flávio du Pin Calmon, Sandilya Bhamidipati, Nadia Fawaz, Branislav Kveton, Pedro Oliveira, Nina Taft
Managing your Private and Public Data: Bringing down Inference Attacks against your Privacy. CoRR (2014)
Amy Zhang, Nadia Fawaz, Stratis Ioannidis, Andrea Montanari
Guess Who Rated This Movie: Identifying Users Through Subspace Clustering. CoRR (2014)
Salman Salamatian, Amy Zhang, Flávio du Pin Calmon, Sandilya Bhamidipati, Nadia Fawaz, Branislav Kveton, Pedro Oliveira, Nina Taft
How to hide the elephant- or the donkey- in the room: Practical privacy against statistical inference for large data. GlobalSIP (2013)
Amy Zhang, Nadia Fawaz, Stratis Ioannidis, Andrea Montanari
Guess Who Rated This Movie: Identifying Users Through Subspace Clustering CoRR (2012)
Amy Zhang, Nadia Fawaz, Stratis Ioannidis, Andrea Montanari
Guess Who Rated This Movie: Identifying Users Through Subspace Clustering. UAI (2012)