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ORCIDAmy Zhang 0001
Person information
- affiliation: UT Austin, TX, USA
- affiliation: Facebook Inc.
- affiliation: McGill University, Department of Computer Science, Montreal, QC, Canada
Other persons with the same name
- Amy Zhang — disambiguation page
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2020 – today
- 2024
[j6]Max Rudolph, Caleb Chuck, Kevin Black, Misha Lvovsky, Scott Niekum, Amy Zhang:
Learning Action-based Representations Using Invariance. RLJ 1: 342-365 (2024)- [j5]Alexander Levine, Peter Stone, Amy Zhang:
Multistep Inverse Is Not All You Need. RLJ 2: 884-925 (2024) - [c35]Prajjwal Bhargava, Rohan Chitnis, Alborz Geramifard, Shagun Sodhani, Amy Zhang:
When should we prefer Decision Transformers for Offline Reinforcement Learning? ICLR 2024 - [c34]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 - [c33]Harshit Sikchi, Rohan Chitnis, Ahmed Touati, Alborz Geramifard, Amy Zhang, Scott Niekum:
Score Models for Offline Goal-Conditioned Reinforcement Learning. ICLR 2024 - [c32]Harshit Sikchi, Qinqing Zheng, Amy Zhang, Scott Niekum:
Dual RL: Unification and New Methods for Reinforcement and Imitation Learning. ICLR 2024 - [c31]Edwin Zhang, Yujie Lu, Shinda Huang, William Yang Wang, Amy Zhang:
Language Control Diffusion: Efficiently Scaling through Space, Time, and Tasks. ICLR 2024 - [c30]Samin Yeasar Arnob, Riyasat Ohib, Sergey M. Plis, Amy Zhang, Alessandro Sordoni, Doina Precup:
Efficient Reinforcement Learning by Discovering Neural Pathways. NeurIPS 2024 - [c29]Zizhao Wang, Jiaheng Hu, Caleb Chuck, Stephen Chen, Roberto Martín-Martín, Amy Zhang, Scott Niekum, Peter Stone:
SkiLD: Unsupervised Skill Discovery Guided by Factor Interactions. NeurIPS 2024 - [i58]Zihan Ding, Amy Zhang, Yuandong Tian, Qinqing Zheng:
Diffusion World Model. CoRR abs/2402.03570 (2024) - [i57]Alexander Levine, Peter Stone, Amy Zhang:
Multistep Inverse Is Not All You Need. CoRR abs/2403.11940 (2024) - [i56]Max Rudolph, Caleb Chuck, Kevin Black, Misha Lvovsky, Scott Niekum, Amy Zhang:
Learning Action-based Representations Using Invariance. CoRR abs/2403.16369 (2024) - [i55]Caleb Chuck, Sankaran Vaidyanathan, Stephen Giguere, Amy Zhang, David Jensen, Scott Niekum:
Automated Discovery of Functional Actual Causes in Complex Environments. CoRR abs/2404.10883 (2024) - [i54]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 abs/2405.03113 (2024) - [i53]Harshit Sikchi, Caleb Chuck, Amy Zhang, Scott Niekum:
A Dual Approach to Imitation Learning from Observations with Offline Datasets. CoRR abs/2406.08805 (2024) - [i52]Philippe Hansen-Estruch, Sriram Vishwanath, Amy Zhang, Manan Tomar:
Unified Auto-Encoding with Masked Diffusion. CoRR abs/2406.17688 (2024) - [i51]Alexander Levine, Peter Stone, Amy Zhang:
Learning a Fast Mixing Exogenous Block MDP using a Single Trajectory. CoRR abs/2410.03016 (2024) - [i50]Zizhao Wang, Jiaheng Hu, Caleb Chuck, Stephen Chen, Roberto Martín-Martín, Amy Zhang, Scott Niekum, Peter Stone:
SkiLD: Unsupervised Skill Discovery Guided by Factor Interactions. CoRR abs/2410.18416 (2024) - [i49]Qinqing Zheng, Mikael Henaff, Amy Zhang, Aditya Grover, Brandon Amos:
Online Intrinsic Rewards for Decision Making Agents from Large Language Model Feedback. CoRR abs/2410.23022 (2024) - [i48]Siddhant Agarwal, Harshit Sikchi, Peter Stone, Amy Zhang:
Proto Successor Measure: Representing the Space of All Possible Solutions of Reinforcement Learning. CoRR abs/2411.19418 (2024) - [i47]Harshit Sikchi, Siddhant Agarwal, Pranaya Jajoo, Samyak Parajuli, Caleb Chuck, Max Rudolph, Peter Stone, Amy Zhang, Scott Niekum:
RL Zero: Zero-Shot Language to Behaviors without any Supervision. CoRR abs/2412.05718 (2024) - [i46]Martin Klissarov, Mikael Henaff, Roberta Raileanu, Shagun Sodhani, Pascal Vincent, Amy Zhang, Pierre-Luc Bacon, Doina Precup, Marlos C. Machado, Pierluca D'Oro:
MaestroMotif: Skill Design from Artificial Intelligence Feedback. CoRR abs/2412.08542 (2024) - [i45]Carl Qi, Dan Haramati, Tal Daniel, Aviv Tamar, Amy Zhang:
EC-Diffuser: Multi-Object Manipulation via Entity-Centric Behavior Generation. CoRR abs/2412.18907 (2024) - 2023
- [j4]Shagun Sodhani, Sergey Levine, Amy Zhang:
Improving Generalization with Approximate Factored Value Functions. Trans. Mach. Learn. Res. 2023 (2023) - [j3]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) - [c28]Mulong Luo, Wenjie Xiong
, Geunbae Lee, Yueying Li, Xiaomeng Yang, Amy Zhang, Yuandong Tian, Hsien-Hsin S. Lee, G. Edward Suh:
AutoCAT: Reinforcement Learning for Automated Exploration of Cache-Timing Attacks. HPCA 2023: 317-332 - [c27]Michael Chang, Alyssa L. Dayan, Franziska Meier, Thomas L. Griffiths, Sergey Levine, Amy Zhang:
Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement. ICLR 2023 - [c26]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 - [c25]Andrew Szot, Amy Zhang, Dhruv Batra, Zsolt Kira, Franziska Meier:
BC-IRL: Learning Generalizable Reward Functions from Demonstrations. ICLR 2023 - [c24]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 - [c23]Yecheng Jason Ma, Vikash Kumar, Amy Zhang, Osbert Bastani, Dinesh Jayaraman:
LIV: Language-Image Representations and Rewards for Robotic Control. ICML 2023: 23301-23320 - [c22]Tongzhou Wang, Antonio Torralba, Phillip Isola, Amy Zhang:
Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learning. ICML 2023: 36411-36430 - [c21]Siddhant Agarwal, Ishan Durugkar, Peter Stone, Amy Zhang:
f-Policy Gradients: A General Framework for Goal-Conditioned RL using f-Divergences. NeurIPS 2023 - [c20]Qiyang Li, Jason Zhang, Dibya Ghosh, Amy Zhang, Sergey Levine:
Accelerating Exploration with Unlabeled Prior Data. NeurIPS 2023 - [c19]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: 2488-2497 - [i44]Harshit Sikchi, Amy Zhang, Scott Niekum:
Imitation from Arbitrary Experience: A Dual Unification of Reinforcement and Imitation Learning Methods. CoRR abs/2302.08560 (2023) - [i43]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 abs/2303.11373 (2023) - [i42]Andrew Szot, Amy Zhang, Dhruv Batra, Zsolt Kira, Franziska Meier:
BC-IRL: Learning Generalizable Reward Functions from Demonstrations. CoRR abs/2303.16194 (2023) - [i41]Tongzhou Wang, Antonio Torralba, Phillip Isola, Amy Zhang:
Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learning. CoRR abs/2304.01203 (2023) - [i40]Prajjwal Bhargava, Rohan Chitnis, Alborz Geramifard, Shagun Sodhani, Amy Zhang:
Sequence Modeling is a Robust Contender for Offline Reinforcement Learning. CoRR abs/2305.14550 (2023) - [i39]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 abs/2306.00958 (2023) - [i38]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 abs/2310.00166 (2023) - [i37]Siddhant Agarwal, Ishan Durugkar, Peter Stone, Amy Zhang:
f-Policy Gradients: A General Framework for Goal Conditioned RL using f-Divergences. CoRR abs/2310.06794 (2023) - [i36]Harshit Sikchi, Rohan Chitnis, Ahmed Touati, Alborz Geramifard, Amy Zhang, Scott Niekum:
Score Models for Offline Goal-Conditioned Reinforcement Learning. CoRR abs/2311.02013 (2023) - [i35]Qiyang Li, Jason Zhang, Dibya Ghosh, Amy Zhang, Sergey Levine:
Accelerating Exploration with Unlabeled Prior Data. CoRR abs/2311.05067 (2023) - 2022
- [c18]Philippe Hansen-Estruch, Amy Zhang, Ashvin Nair, Patrick Yin, Sergey Levine:
Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learning. ICML 2022: 8407-8426 - [c17]Tongzhou Wang, Simon S. Du, Antonio Torralba, Phillip Isola, Amy Zhang, Yuandong Tian:
Denoised MDPs: Learning World Models Better Than the World Itself. ICML 2022: 22591-22612 - [c16]Annie Xie, Shagun Sodhani, Chelsea Finn, Joelle Pineau, Amy Zhang:
Robust Policy Learning over Multiple Uncertainty Sets. ICML 2022: 24414-24429 - [c15]Qinqing Zheng, Amy Zhang, Aditya Grover:
Online Decision Transformer. ICML 2022: 27042-27059 - [c14]Shagun Sodhani, Franziska Meier, Joelle Pineau, Amy Zhang:
Block Contextual MDPs for Continual Learning. L4DC 2022: 608-623 - [i34]Qinqing Zheng, Amy Zhang, Aditya Grover:
Online Decision Transformer. CoRR abs/2202.05607 (2022) - [i33]Annie Xie, Shagun Sodhani, Chelsea Finn, Joelle Pineau, Amy Zhang:
Robust Policy Learning over Multiple Uncertainty Sets. CoRR abs/2202.07013 (2022) - [i32]Philippe Hansen-Estruch, Amy Zhang, Ashvin Nair, Patrick Yin, Sergey Levine:
Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learning. CoRR abs/2204.13060 (2022) - [i31]Tongzhou Wang, Simon S. Du, Antonio Torralba, Phillip Isola, Amy Zhang, Yuandong Tian:
Denoised MDPs: Learning World Models Better Than the World Itself. CoRR abs/2206.15477 (2022) - [i30]Mulong Luo, Wenjie Xiong, Geunbae Lee, Yueying Li, Xiaomeng Yang, Amy Zhang, Yuandong Tian, Hsien-Hsin S. Lee, G. Edward Suh:
AutoCAT: Reinforcement Learning for Automated Exploration of Cache Timing-Channel Attacks. CoRR abs/2208.08025 (2022) - [i29]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 abs/2210.00030 (2022) - [i28]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 abs/2210.00999 (2022) - [i27]Edwin Zhang, Yujie Lu, William Wang, Amy Zhang:
LAD: Language Augmented Diffusion for Reinforcement Learning. CoRR abs/2210.15629 (2022) - 2021
- [c13]Denis Yarats, Amy Zhang, Ilya Kostrikov, Brandon Amos, Joelle Pineau, Rob Fergus:
Improving Sample Efficiency in Model-Free Reinforcement Learning from Images. AAAI 2021: 10674-10681 - [c12]Amy Zhang, Rowan Thomas McAllister, Roberto Calandra, Yarin Gal, Sergey Levine:
Learning Invariant Representations for Reinforcement Learning without Reconstruction. ICLR 2021 - [c11]Amy Zhang, Shagun Sodhani, Khimya Khetarpal, Joelle Pineau:
Learning Robust State Abstractions for Hidden-Parameter Block MDPs. ICLR 2021 - [c10]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: 5815-5826 - [c9]Shagun Sodhani, Amy Zhang, Joelle Pineau:
Multi-Task Reinforcement Learning with Context-based Representations. ICML 2021: 9767-9779 - [c8]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: 25502-25515 - [i26]Shagun Sodhani, Amy Zhang, Joelle Pineau:
Multi-Task Reinforcement Learning with Context-based Representations. CoRR abs/2102.06177 (2021) - [i25]Manan Tomar, Amy Zhang, Roberto Calandra, Matthew E. Taylor, Joelle Pineau:
Model-Invariant State Abstractions for Model-Based Reinforcement Learning. CoRR abs/2102.09850 (2021) - [i24]Luis Pineda, Brandon Amos, Amy Zhang, Nathan O. Lambert, Roberto Calandra:
MBRL-Lib: A Modular Library for Model-based Reinforcement Learning. CoRR abs/2104.10159 (2021) - [i23]Weitong Zhang, Jiafan He, Dongruo Zhou, Amy Zhang, Quanquan Gu:
Provably Efficient Representation Learning in Low-rank Markov Decision Processes. CoRR abs/2106.11935 (2021) - [i22]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 abs/2107.06277 (2021) - [i21]Shagun Sodhani, Franziska Meier, Joelle Pineau, Amy Zhang:
Block Contextual MDPs for Continual Learning. CoRR abs/2110.06972 (2021) - [i20]Manan Tomar, Utkarsh A. Mishra, Amy Zhang, Matthew E. Taylor:
Learning Representations for Pixel-based Control: What Matters and Why? CoRR abs/2111.07775 (2021) - 2020
- [c7]Amy Zhang, Clare Lyle, Shagun Sodhani, Angelos Filos, Marta Kwiatkowska, Joelle Pineau, Yarin Gal, Doina Precup:
Invariant Causal Prediction for Block MDPs. ICML 2020: 11214-11224 - [c6]Ge Yang, Amy Zhang, Ari S. Morcos, Joelle Pineau, Pieter Abbeel, Roberto Calandra:
Plan2Vec: Unsupervised Representation Learning by Latent Plans. L4DC 2020: 935-946 - [c5]Ahmed Touati, Amy Zhang, Joelle Pineau, Pascal Vincent:
Stable Policy Optimization via Off-Policy Divergence Regularization. UAI 2020: 1328-1337 - [i19]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 abs/2003.00688 (2020) - [i18]Ahmed Touati, Amy Zhang, Joelle Pineau, Pascal Vincent:
Stable Policy Optimization via Off-Policy Divergence Regularization. CoRR abs/2003.04108 (2020) - [i17]Amy Zhang, Clare Lyle, Shagun Sodhani, Angelos Filos, Marta Kwiatkowska, Joelle Pineau, Yarin Gal, Doina Precup:
Invariant Causal Prediction for Block MDPs. CoRR abs/2003.06016 (2020) - [i16]Ge Yang, Amy Zhang, Ari S. Morcos, Joelle Pineau, Pieter Abbeel, Roberto Calandra:
Plan2Vec: Unsupervised Representation Learning by Latent Plans. CoRR abs/2005.03648 (2020) - [i15]Amy Zhang, Rowan McAllister, Roberto Calandra, Yarin Gal, Sergey Levine:
Learning Invariant Representations for Reinforcement Learning without Reconstruction. CoRR abs/2006.10742 (2020) - [i14]Amy Zhang, Shagun Sodhani, Khimya Khetarpal, Joelle Pineau:
Multi-Task Reinforcement Learning as a Hidden-Parameter Block MDP. CoRR abs/2007.07206 (2020) - [i13]Melissa Mozifian, Amy Zhang, Joelle Pineau, David Meger:
Intervention Design for Effective Sim2Real Transfer. CoRR abs/2012.02055 (2020)
2010 – 2019
- 2019
- [i12]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 abs/1906.10437 (2019) - [i11]Denis Yarats, Amy Zhang, Ilya Kostrikov, Brandon Amos, Joelle Pineau, Rob Fergus:
Improving Sample Efficiency in Model-Free Reinforcement Learning from Images. CoRR abs/1910.01741 (2019) - 2018
- [c4]Amy Zhang, Harsh Satija, Joelle Pineau:
Decoupling Dynamics and Reward for Transfer Learning. ICLR (Workshop) 2018 - [c3]Amy Zhang, Sainbayar Sukhbaatar, Adam Lerer, Arthur Szlam, Rob Fergus:
Composable Planning with Attributes. ICML 2018: 5837-5846 - [i10]Amy Zhang, Adam Lerer, Sainbayar Sukhbaatar, Rob Fergus, Arthur Szlam:
Composable Planning with Attributes. CoRR abs/1803.00512 (2018) - [i9]Amy Zhang, Harsh Satija, Joelle Pineau:
Decoupling Dynamics and Reward for Transfer Learning. CoRR abs/1804.10689 (2018) - [i8]Amy Zhang, Nicolas Ballas, Joelle Pineau:
A Dissection of Overfitting and Generalization in Continuous Reinforcement Learning. CoRR abs/1806.07937 (2018) - [i7]Amy Zhang, Yuxin Wu, Joelle Pineau:
Natural Environment Benchmarks for Reinforcement Learning. CoRR abs/1811.06032 (2018) - 2017
- [i6]Amy Zhang, Xianming Liu, Andreas Gros, Tobias Tiecke:
Building Detection from Satellite Images on a Global Scale. CoRR abs/1707.08952 (2017) - [i5]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 abs/1712.05839 (2017) - 2016
- [i4]Xianming Liu, Amy Zhang, Tobias Tiecke, Andreas Gros, Thomas S. Huang:
Feedback Neural Network for Weakly Supervised Geo-Semantic Segmentation. CoRR abs/1612.02766 (2016) - 2015
- [j2]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): 1240-1255 (2015) - [j1]Sandilya Bhamidipati, Nadia Fawaz, Branislav Kveton, Amy Zhang:
PriView: Personalized Media Consumption Meets Privacy against Inference Attacks. IEEE Softw. 32(4): 53-59 (2015) - 2014
- [i3]Amy Zhang, Nadia Fawaz, Stratis Ioannidis, Andrea Montanari:
Guess Who Rated This Movie: Identifying Users Through Subspace Clustering. CoRR abs/1408.2055 (2014) - [i2]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 abs/1408.3698 (2014) - 2013
- [c2]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: 269-272 - 2012
- [c1]Amy Zhang, Nadia Fawaz, Stratis Ioannidis, Andrea Montanari:
Guess Who Rated This Movie: Identifying Users Through Subspace Clustering. UAI 2012: 944-953 - [i1]Amy Zhang, Nadia Fawaz, Stratis Ioannidis, Andrea Montanari:
Guess Who Rated This Movie: Identifying Users Through Subspace Clustering. CoRR abs/1208.1544 (2012)
Coauthor Index
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