Stephan Zheng

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 43

Top Topics

Reinforcement Learning

Top Venues

Trans. Mach. Learn. Res.

Publications

Edwin Zhang, Sadie Zhao, Tonghan Wang, Safwan Hossain, Henry Gasztowtt, Stephan Zheng, David C. Parkes, Milind Tambe, Yiling Chen
Social Environment Design. CoRR (2024)
Xintong Wang, Gary Qiurui Ma, Alon Eden, Clara Li, Alexander Trott, Stephan Zheng, David C. Parkes
Platform Behavior under Market Shocks: A Simulation Framework and Reinforcement-Learning Based Study. WWW (2023)
Arundhati Banerjee, Soham Phade, Stefano Ermon, Stephan Zheng
MERMAIDE: Learning to Align Learners using Model-Based Meta-Learning. CoRR (2023)
Arundhati Banerjee, Soham R. Phade, Stefano Ermon, Stephan Zheng
MERMAIDE: Learning to Align Learners using Model-Based Meta-Learning. Trans. Mach. Learn. Res. 2023 (2023)
Eric Zhao, Alexander R. Trott, Caiming Xiong, Stephan Zheng
Learning to Play General-Sum Games against Multiple Boundedly Rational Agents. AAAI (2023)
Michael Curry, Alexander Trott, Soham Phade, Yu Bai, Stephan Zheng
Learning Solutions in Large Economic Networks using Deep Multi-Agent Reinforcement Learning. AAMAS (2023)
Yoshua Bengio, Prateek Gupta, Lu Li, Soham Phade, Sunil Srinivasa, Andrew Williams, Tianyu Zhang, Yang Zhang, Stephan Zheng
AI For Global Climate Cooperation 2023 Competition Proceedings. CoRR (2023)
Tian Lan, Sunil Srinivasa, Huan Wang, Stephan Zheng
WarpDrive: Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU. J. Mach. Learn. Res. 23 (2022)
Xintong Wang, Gary Qiurui Ma, Alon Eden, Clara Li, Alexander Trott, Stephan Zheng, David C. Parkes
Using Reinforcement Learning to Study Platform Economies under Market Shocks. CoRR (2022)
Tong Mu, Stephan Zheng, Alexander R. Trott
Modeling Bounded Rationality in Multi-Agent Simulations Using Rationally Inattentive Reinforcement Learning. Trans. Mach. Learn. Res. 2022 (2022)
Michael Curry, Alexander Trott, Soham Phade, Yu Bai, Stephan Zheng
Finding General Equilibria in Many-Agent Economic Simulations Using Deep Reinforcement Learning. CoRR (2022)
Tong Mu, Stephan Zheng, Alexander Trott
Solving Dynamic Principal-Agent Problems with a Rationally Inattentive Principal. CoRR (2022)
Tianyu Zhang, Andrew Williams, Soham Phade, Sunil Srinivasa, Yang Zhang, Prateek Gupta, Yoshua Bengio, Stephan Zheng
AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N. CoRR (2022)
Tian Lan, Sunil Srinivasa, Stephan Zheng
WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU. CoRR (2021)
Stephan Zheng, Alexander Trott, Sunil Srinivasa, David C. Parkes, Richard Socher
The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning. CoRR (2021)
Alexander Trott, Sunil Srinivasa, Douwe van der Wal, Sebastien Haneuse, Stephan Zheng
Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI Economist. CoRR (2021)
Eric Zhao, Alexander R. Trott, Caiming Xiong, Stephan Zheng
ERMAS: Becoming Robust to Reward Function Sim-to-Real Gaps in Multi-Agent Simulations. CoRR (2021)
Alexander Lavin, Hector Zenil, Brooks Paige, David Krakauer, Justin Gottschlich, Tim Mattson, Anima Anandkumar, Sanjay Choudry, Kamil Rocki, Atilim Günes Baydin, Carina Prunkl, Olexandr Isayev, Erik Peterson, Peter L. McMahon, Jakob Macke, Kyle Cranmer, Jiaxin Zhang, Haruko M. Wainwright, Adi Hanuka, Manuela Veloso, Samuel Assefa, Stephan Zheng, Avi Pfeffer
Simulation Intelligence: Towards a New Generation of Scientific Methods. CoRR (2021)
Karan Goel, Nazneen Fatema Rajani, Jesse Vig, Samson Tan, Jason Wu, Stephan Zheng, Caiming Xiong, Mohit Bansal, Christopher Ré
Robustness Gym: Unifying the NLP Evaluation Landscape. CoRR (2021)
Jung Yeon Park, Kenneth Theo Carr, Stephan Zheng, Yisong Yue, Rose Yu
Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis. CoRR (2020)
Nazneen Fatema Rajani, Rui Zhang, Yi Chern Tan, Stephan Zheng, Jeremy Weiss, Aadit Vyas, Abhijit Gupta, Caiming Xiong, Richard Socher, Dragomir R. Radev
ESPRIT: Explaining Solutions to Physical Reasoning Tasks. CoRR (2020)
Stephan Zheng, Alexander Trott, Sunil Srinivasa, Nikhil Naik, Melvin Gruesbeck, David C. Parkes, Richard Socher
The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies. CoRR (2020)
Nazneen Fatema Rajani, Rui Zhang, Yi Chern Tan, Stephan Zheng, Jeremy Weiss, Aadit Vyas, Abhijit Gupta, Caiming Xiong, Richard Socher, Dragomir R. Radev
ESPRIT: Explaining Solutions to Physical Reasoning Tasks. ACL (2020)
Ian T. Foster, David C. Parkes, Stephan Zheng
The Rise of AI-Driven Simulators: Building a New Crystal Ball. CoRR (2020)
Jung Yeon Park, Kenneth Theo Carr, Stephan Zheng, Yisong Yue, Rose Yu
Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis. ICML (2020)
Michael Shum, Stephan Zheng, Wojciech Kryscinski, Caiming Xiong, Richard Socher
Sketch-Fill-A-R: A Persona-Grounded Chit-Chat Generation Framework. CoRR (2019)
Huan Wang, Stephan Zheng, Caiming Xiong, Richard Socher
On the Generalization Gap in Reparameterizable Reinforcement Learning. ICML (2019)
Yukai Liu, Rose Yu, Stephan Zheng, Eric Zhan, Yisong Yue
NAOMI: Non-Autoregressive Multiresolution Sequence Imputation. NeurIPS (2019)
Alexander Trott, Stephan Zheng, Caiming Xiong, Richard Socher
Keeping Your Distance: Solving Sparse Reward Tasks Using Self-Balancing Shaped Rewards. CoRR (2019)
Yukai Liu, Rose Yu, Stephan Zheng, Eric Zhan, Yisong Yue
NAOMI: Non-Autoregressive Multiresolution Sequence Imputation. CoRR (2019)
Eric Zhan, Stephan Zheng, Yisong Yue, Long Sha, Patrick Lucey
Generating Multi-Agent Trajectories using Programmatic Weak Supervision. ICLR (Poster) (2019)
Wenling Shang, Alexander Trott, Stephan Zheng, Caiming Xiong, Richard Socher
Learning World Graphs to Accelerate Hierarchical Reinforcement Learning. CoRR (2019)
Alexander Trott, Stephan Zheng, Caiming Xiong, Richard Socher
Keeping Your Distance: Solving Sparse Reward Tasks Using Self-Balancing Shaped Rewards. NeurIPS (2019)
Huan Wang, Stephan Zheng, Caiming Xiong, Richard Socher
On the Generalization Gap in Reparameterizable Reinforcement Learning. CoRR (2019)
Stephan Zheng, Rose Yu, Yisong Yue
Multi-resolution Tensor Learning for Large-Scale Spatial Data. CoRR (2018)
Sumanth Dathathri, Stephan Zheng, Richard M. Murray, Yisong Yue
Detecting Adversarial Examples via Neural Fingerprinting. CoRR (2018)
Eric Zhan, Stephan Zheng, Yisong Yue, Long Sha, Patrick Lucey
Generative Multi-Agent Behavioral Cloning. CoRR (2018)
Long Sha, Patrick Lucey, Stephan Zheng, Taehwan Kim, Yisong Yue, Sridha Sridharan
Fine-Grained Retrieval of Sports Plays using Tree-Based Alignment of Trajectories. CoRR (2017)
Rose Yu, Stephan Zheng, Anima Anandkumar, Yisong Yue
Long-term Forecasting using Tensor-Train RNNs. CoRR (2017)
Stephan Zheng, Yisong Yue, Patrick Lucey
Generating Long-term Trajectories Using Deep Hierarchical Networks. CoRR (2017)
Stephan Zheng, Yang Song, Thomas Leung, Ian J. Goodfellow
Improving the Robustness of Deep Neural Networks via Stability Training. CVPR (2016)
Stephan Zheng, Yang Song, Thomas Leung, Ian J. Goodfellow
Improving the Robustness of Deep Neural Networks via Stability Training. CoRR (2016)
Stephan Zheng, Yisong Yue, Jennifer A. Hobbs
Generating Long-term Trajectories Using Deep Hierarchical Networks. NIPS (2016)