Amy Zhang

According to our database, Amy Zhang authored at least 65 papers between 2012 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Online Intrinsic Rewards for Decision Making Agents from Large Language Model Feedback.

Qinqing Zheng, Mikael Henaff, Amy Zhang, Aditya Grover, Brandon Amos

CoRR, 2024

SkiLD: Unsupervised Skill Discovery Guided by Factor Interactions.

Zizhao Wang, Jiaheng Hu, Caleb Chuck, Stephen Chen, Roberto Martín-Martín, Amy Zhang, Scott Niekum, Peter Stone

CoRR, 2024

Learning a Fast Mixing Exogenous Block MDP using a Single Trajectory.

Alexander Levine, Peter Stone, Amy Zhang

CoRR, 2024

Unified Auto-Encoding with Masked Diffusion.

Philippe Hansen-Estruch, Sriram Vishwanath, Amy Zhang, Manan Tomar

CoRR, 2024

A Dual Approach to Imitation Learning from Observations with Offline Datasets.

Harshit Sikchi, Caleb Chuck, Amy Zhang, 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, Anthony Wang, Peter Stone, Amy Zhang, Scott Niekum

CoRR, 2024

Automated Discovery of Functional Actual Causes in Complex Environments.

Caleb Chuck, Sankaran Vaidyanathan, Stephen Giguere, Amy Zhang, David Jensen, Scott Niekum

CoRR, 2024

Diffusion World Model.

Zihan Ding, Amy Zhang, Yuandong Tian, Qinqing Zheng

CoRR, 2024

Learning Action-based Representations Using Invariance.

Max Rudolph, Caleb Chuck, Kevin Black, Misha Lvovsky, Scott Niekum, Amy Zhang

RLJ, 2024

Multistep Inverse Is Not All You Need.

Alexander Levine, Peter Stone, Amy Zhang

RLJ, 2024

Language Control Diffusion: Efficiently Scaling through Space, Time, and Tasks.

Edwin Zhang, Yujie Lu, Shinda Huang, William Yang Wang, Amy Zhang

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Dual RL: Unification and New Methods for Reinforcement and Imitation Learning.

Harshit Sikchi, Qinqing Zheng, Amy Zhang, Scott Niekum

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Score Models for Offline Goal-Conditioned Reinforcement Learning.

Harshit Sikchi, Rohan Chitnis, Ahmed Touati, Alborz Geramifard, Amy Zhang, Scott Niekum

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Motif: Intrinsic Motivation from Artificial Intelligence Feedback.

Martin Klissarov, Pierluca D'Oro, Shagun Sodhani, Roberta Raileanu, Pierre-Luc Bacon, Pascal Vincent, Amy Zhang, Mikael Henaff

Proceedings of the Twelfth International Conference on Learning Representations, 2024

When should we prefer Decision Transformers for Offline Reinforcement Learning?

Prajjwal Bhargava, Rohan Chitnis, Alborz Geramifard, Shagun Sodhani, Amy Zhang

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Learning Representations for Pixel-based Control: What Matters and Why?

Manan Tomar, Utkarsh A. Mishra, Amy Zhang, Matthew E. Taylor

Trans. Mach. Learn. Res., 2023

Improving Generalization with Approximate Factored Value Functions.

Shagun Sodhani, Sergey Levine, Amy Zhang

Trans. Mach. Learn. Res., 2023

LIV: Language-Image Representations and Rewards for Robotic Control.

Yecheng Jason Ma, William Liang, Vaidehi Som, Vikash Kumar, Amy Zhang, Osbert Bastani, Dinesh Jayaraman

CoRR, 2023

Sequence Modeling is a Robust Contender for Offline Reinforcement Learning.

Prajjwal Bhargava, Rohan Chitnis, Alborz Geramifard, Shagun Sodhani, Amy Zhang

CoRR, 2023

Neural Constraint Satisfaction: Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement.

Michael Chang, Alyssa L. Dayan, Franziska Meier, Thomas L. Griffiths, Sergey Levine, Amy Zhang

CoRR, 2023

Imitation from Arbitrary Experience: A Dual Unification of Reinforcement and Imitation Learning Methods.

Harshit Sikchi, Amy Zhang, Scott Niekum

CoRR, 2023

Provably efficient representation selection in Low-rank Markov Decision Processes: from online to offline RL.

Weitong Zhang, Jiafan He, Dongruo Zhou, Amy Zhang, Quanquan Gu

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Accelerating Exploration with Unlabeled Prior Data.

Qiyang Li, Jason Zhang, Dibya Ghosh, Amy Zhang, Sergey Levine

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

f-Policy Gradients: A General Framework for Goal-Conditioned RL using f-Divergences.

Siddhant Agarwal, Ishan Durugkar, Peter Stone, Amy Zhang

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

LIV: Language-Image Representations and Rewards for Robotic Control.

Yecheng Jason Ma, Vikash Kumar, Amy Zhang, Osbert Bastani, Dinesh Jayaraman

Proceedings of the International Conference on Machine Learning, 2023

Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learning.

Tongzhou Wang, Antonio Torralba, Phillip Isola, Amy Zhang

Proceedings of the International Conference on Machine Learning, 2023

Latent State Marginalization as a Low-cost Approach for Improving Exploration.

Dinghuai Zhang, Aaron C. Courville, Yoshua Bengio, Qinqing Zheng, Amy Zhang, Ricky T. Q. Chen

Proceedings of the Eleventh International Conference on Learning Representations, 2023

BC-IRL: Learning Generalizable Reward Functions from Demonstrations.

Andrew Szot, Amy Zhang, Dhruv Batra, Zsolt Kira, Franziska Meier

Proceedings of the Eleventh International Conference on Learning Representations, 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

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement.

Michael Chang, Alyssa L. Dayan, Franziska Meier, Thomas L. Griffiths, Sergey Levine, Amy Zhang

Proceedings of the Eleventh International Conference on Learning Representations, 2023

AutoCAT: Reinforcement Learning for Automated Exploration of Cache-Timing Attacks.

Mulong Luo, Wenjie Xiong, Geunbae Lee, Yueying Li, Xiaomeng Yang, Amy Zhang, Yuandong Tian, Hsien-Hsin S. Lee, G. Edward Suh

Proceedings of the IEEE International Symposium on High-Performance Computer Architecture, 2023

2022

LAD: Language Augmented Diffusion for Reinforcement Learning.

Edwin Zhang, Yujie Lu, William Wang, Amy Zhang

CoRR, 2022

AutoCAT: Reinforcement Learning for Automated Exploration of Cache Timing-Channel Attacks.

Mulong Luo, Wenjie Xiong, Geunbae Lee, Yueying Li, Xiaomeng Yang, Amy Zhang, Yuandong Tian, Hsien-Hsin S. Lee, G. Edward Suh

CoRR, 2022

Block Contextual MDPs for Continual Learning.

Shagun Sodhani, Franziska Meier, Joelle Pineau, Amy Zhang

Proceedings of the Learning for Dynamics and Control Conference, 2022

Online Decision Transformer.

Qinqing Zheng, Amy Zhang, Aditya Grover

Proceedings of the International Conference on Machine Learning, 2022

Robust Policy Learning over Multiple Uncertainty Sets.

Annie Xie, Shagun Sodhani, Chelsea Finn, Joelle Pineau, Amy Zhang

Proceedings of the International Conference on Machine Learning, 2022

Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learning.

Philippe Hansen-Estruch, Amy Zhang, Ashvin Nair, Patrick Yin, Sergey Levine

Proceedings of the International Conference on Machine Learning, 2022

Denoised MDPs: Learning World Models Better Than the World Itself.

Tongzhou Wang, Simon S. Du, Antonio Torralba, Phillip Isola, Amy Zhang, Yuandong Tian

Proceedings of the International Conference on Machine Learning, 2022

2021

Provably Efficient Representation Learning in Low-rank Markov Decision Processes.

Weitong Zhang, Jiafan He, Dongruo Zhou, Amy Zhang, Quanquan Gu

CoRR, 2021

MBRL-Lib: A Modular Library for Model-based Reinforcement Learning.

Luis Pineda, Brandon Amos, Amy Zhang, Nathan O. Lambert, Roberto Calandra

CoRR, 2021

Model-Invariant State Abstractions for Model-Based Reinforcement Learning.

Manan Tomar, Amy Zhang, Roberto Calandra, Matthew E. Taylor, Joelle Pineau

CoRR, 2021

Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability.

Dibya Ghosh, Jad Rahme, Aviral Kumar, Amy Zhang, Ryan P. Adams, Sergey Levine

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Multi-Task Reinforcement Learning with Context-based Representations.

Shagun Sodhani, Amy Zhang, Joelle Pineau

Proceedings of the 38th International Conference on Machine Learning, 2021

Out-of-Distribution Generalization via Risk Extrapolation (REx).

David Krueger, Ethan Caballero, Jörn-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Rémi Le Priol, Aaron C. Courville

Proceedings of the 38th International Conference on Machine Learning, 2021

Learning Robust State Abstractions for Hidden-Parameter Block MDPs.

Amy Zhang, Shagun Sodhani, Khimya Khetarpal, Joelle Pineau

Proceedings of the 9th International Conference on Learning Representations, 2021

Learning Invariant Representations for Reinforcement Learning without Reconstruction.

Amy Zhang, Rowan Thomas McAllister, Roberto Calandra, Yarin Gal, Sergey Levine

Proceedings of the 9th International Conference on Learning Representations, 2021

Improving Sample Efficiency in Model-Free Reinforcement Learning from Images.

Denis Yarats, Amy Zhang, Ilya Kostrikov, Brandon Amos, Joelle Pineau, Rob Fergus

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Intervention Design for Effective Sim2Real Transfer.

Melissa Mozifian, Amy Zhang, Joelle Pineau, David Meger

CoRR, 2020

Multi-Task Reinforcement Learning as a Hidden-Parameter Block MDP.

Amy Zhang, Shagun Sodhani, Khimya Khetarpal, Joelle Pineau

CoRR, 2020

Out-of-Distribution Generalization via Risk Extrapolation (REx).

David Krueger, Ethan Caballero, Jörn-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Rémi Le Priol, Aaron C. Courville

CoRR, 2020

Stable Policy Optimization via Off-Policy Divergence Regularization.

Ahmed Touati, Amy Zhang, Joelle Pineau, Pascal Vincent

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Plan2Vec: Unsupervised Representation Learning by Latent Plans.

Ge Yang, Amy Zhang, Ari S. Morcos, Joelle Pineau, Pieter Abbeel, Roberto Calandra

Proceedings of the 2nd Annual Conference on Learning for Dynamics and Control, 2020

Invariant Causal Prediction for Block MDPs.

Amy Zhang, Clare Lyle, Shagun Sodhani, Angelos Filos, Marta Kwiatkowska, Joelle Pineau, Yarin Gal, Doina Precup

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Learning Causal State Representations of Partially Observable Environments.

Amy Zhang, Zachary C. Lipton, Luis Pineda, Kamyar Azizzadenesheli, Anima Anandkumar, Laurent Itti, Joelle Pineau, Tommaso Furlanello

CoRR, 2019

2018

Natural Environment Benchmarks for Reinforcement Learning.

Amy Zhang, Yuxin Wu, Joelle Pineau

CoRR, 2018

A Dissection of Overfitting and Generalization in Continuous Reinforcement Learning.

Amy Zhang, Nicolas Ballas, Joelle Pineau

CoRR, 2018

Composable Planning with Attributes.

Amy Zhang, Sainbayar Sukhbaatar, Adam Lerer, Arthur Szlam, Rob Fergus

Proceedings of the 35th International Conference on Machine Learning, 2018

Decoupling Dynamics and Reward for Transfer Learning.

Amy Zhang, Harsh Satija, Joelle Pineau

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Mapping the world population one building at a time.

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

CoRR, 2017

Building Detection from Satellite Images on a Global Scale.

Amy Zhang, Xianming Liu, Andreas Gros, Tobias Tiecke

CoRR, 2017

2016

Feedback Neural Network for Weakly Supervised Geo-Semantic Segmentation.

Xianming Liu, Amy Zhang, Tobias Tiecke, Andreas Gros, Thomas S. Huang

CoRR, 2016

2015

PriView: Personalized Media Consumption Meets Privacy against Inference Attacks.

Sandilya Bhamidipati, Nadia Fawaz, Branislav Kveton, Amy Zhang

IEEE Softw., 2015

Managing Your Private and Public Data: Bringing Down Inference Attacks Against Your Privacy.

Salman Salamatian, Amy Zhang, Flávio du Pin Calmon, Sandilya Bhamidipati, Nadia Fawaz, Branislav Kveton, Pedro Oliveira, Nina Taft

IEEE J. Sel. Top. Signal Process., 2015

2013

How to hide the elephant- or the donkey- in the room: Practical privacy against statistical inference for large data.

Salman Salamatian, Amy Zhang, Flávio du Pin Calmon, Sandilya Bhamidipati, Nadia Fawaz, Branislav Kveton, Pedro Oliveira, Nina Taft

Proceedings of the IEEE Global Conference on Signal and Information Processing, 2013

2012

Guess Who Rated This Movie: Identifying Users Through Subspace Clustering.

Amy Zhang, Nadia Fawaz, Stratis Ioannidis, Andrea Montanari

Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, 2012