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Stephan Zheng
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2020 – today
- 2024
- [c12]Edwin Zhang, Sadie Zhao, Tonghan Wang, Safwan Hossain, Henry Gasztowtt, Stephan Zheng, David C. Parkes, Milind Tambe, Yiling Chen:
Position: Social Environment Design Should be Further Developed for AI-based Policy-Making. ICML 2024 - [i29]Edwin Zhang, Sadie Zhao, Tonghan Wang, Safwan Hossain, Henry Gasztowtt, Stephan Zheng, David C. Parkes, Milind Tambe, Yiling Chen:
Social Environment Design. CoRR abs/2402.14090 (2024) - 2023
- [j3]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) - [c11]Eric Zhao, Alexander R. Trott, Caiming Xiong, Stephan Zheng:
Learning to Play General-Sum Games against Multiple Boundedly Rational Agents. AAAI 2023: 11781-11789 - [c10]Michael Curry, Alexander Trott, Soham Phade, Yu Bai, Stephan Zheng:
Learning Solutions in Large Economic Networks using Deep Multi-Agent Reinforcement Learning. AAMAS 2023: 2760-2762 - [c9]Xintong Wang
, Gary Qiurui Ma
Platform Behavior under Market Shocks: A Simulation Framework and Reinforcement-Learning Based Study. WWW 2023: 3592-3602 - [i28]Arundhati Banerjee, Soham Phade, Stefano Ermon, Stephan Zheng:
MERMAIDE: Learning to Align Learners using Model-Based Meta-Learning. CoRR abs/2304.04668 (2023) - [i27]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 abs/2307.06951 (2023) - 2022
- [j2]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: 316:1-316:6 (2022) - [j1]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) - [i26]Michael Curry, Alexander Trott, Soham Phade, Yu Bai, Stephan Zheng:
Finding General Equilibria in Many-Agent Economic Simulations Using Deep Reinforcement Learning. CoRR abs/2201.01163 (2022) - [i25]Tong Mu, Stephan Zheng, Alexander Trott:
Solving Dynamic Principal-Agent Problems with a Rationally Inattentive Principal. CoRR abs/2202.01691 (2022) - [i24]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 abs/2203.13395 (2022) - [i23]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 abs/2208.07004 (2022) - 2021
- [i22]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 abs/2101.04840 (2021) - [i21]Eric Zhao, Alexander R. Trott, Caiming Xiong, Stephan Zheng:
ERMAS: Becoming Robust to Reward Function Sim-to-Real Gaps in Multi-Agent Simulations. CoRR abs/2106.05492 (2021) - [i20]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 abs/2108.02755 (2021) - [i19]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 abs/2108.02904 (2021) - [i18]Tian Lan, Sunil Srinivasa, Stephan Zheng:
WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU. CoRR abs/2108.13976 (2021) - [i17]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 H. 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 abs/2112.03235 (2021) - 2020
- [c8]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: 7906-7917 - [c7]Jung Yeon Park, Kenneth Theo Carr, Stephan Zheng, Yisong Yue, Rose Yu:
Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis. ICML 2020: 7499-7509 - [i16]Jung Yeon Park, Kenneth Theo Carr, Stephan Zheng, Yisong Yue, Rose Yu:
Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis. CoRR abs/2002.05578 (2020) - [i15]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 abs/2004.13332 (2020) - [i14]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 abs/2005.00730 (2020) - [i13]Ian T. Foster, David C. Parkes, Stephan Zheng:
The Rise of AI-Driven Simulators: Building a New Crystal Ball. CoRR abs/2012.06049 (2020)
2010 – 2019
- 2019
- [c6]Eric Zhan, Stephan Zheng, Yisong Yue, Long Sha, Patrick Lucey:
Generating Multi-Agent Trajectories using Programmatic Weak Supervision. ICLR (Poster) 2019 - [c5]Huan Wang, Stephan Zheng, Caiming Xiong, Richard Socher:
On the Generalization Gap in Reparameterizable Reinforcement Learning. ICML 2019: 6648-6658 - [c4]Alexander Trott, Stephan Zheng, Caiming Xiong, Richard Socher:
Keeping Your Distance: Solving Sparse Reward Tasks Using Self-Balancing Shaped Rewards. NeurIPS 2019: 10376-10386 - [c3]Yukai Liu, Rose Yu, Stephan Zheng, Eric Zhan, Yisong Yue:
NAOMI: Non-Autoregressive Multiresolution Sequence Imputation. NeurIPS 2019: 11236-11246 - [i12]Yukai Liu, Rose Yu, Stephan Zheng, Eric Zhan, Yisong Yue:
NAOMI: Non-Autoregressive Multiresolution Sequence Imputation. CoRR abs/1901.10946 (2019) - [i11]Huan Wang, Stephan Zheng, Caiming Xiong, Richard Socher:
On the Generalization Gap in Reparameterizable Reinforcement Learning. CoRR abs/1905.12654 (2019) - [i10]Wenling Shang, Alexander Trott, Stephan Zheng, Caiming Xiong, Richard Socher:
Learning World Graphs to Accelerate Hierarchical Reinforcement Learning. CoRR abs/1907.00664 (2019) - [i9]Michael Shum, Stephan Zheng, Wojciech Kryscinski, Caiming Xiong, Richard Socher:
Sketch-Fill-A-R: A Persona-Grounded Chit-Chat Generation Framework. CoRR abs/1910.13008 (2019) - [i8]Alexander Trott, Stephan Zheng, Caiming Xiong, Richard Socher:
Keeping Your Distance: Solving Sparse Reward Tasks Using Self-Balancing Shaped Rewards. CoRR abs/1911.01417 (2019) - 2018
- [i7]Stephan Zheng, Rose Yu, Yisong Yue:
Multi-resolution Tensor Learning for Large-Scale Spatial Data. CoRR abs/1802.06825 (2018) - [i6]Sumanth Dathathri, Stephan Zheng, Richard M. Murray, Yisong Yue:
Detecting Adversarial Examples via Neural Fingerprinting. CoRR abs/1803.03870 (2018) - [i5]Eric Zhan, Stephan Zheng, Yisong Yue, Long Sha, Patrick Lucey:
Generative Multi-Agent Behavioral Cloning. CoRR abs/1803.07612 (2018) - 2017
- [i4]Stephan Zheng, Yisong Yue, Patrick Lucey:
Generating Long-term Trajectories Using Deep Hierarchical Networks. CoRR abs/1706.07138 (2017) - [i3]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 abs/1710.02255 (2017) - [i2]Rose Yu, Stephan Zheng, Anima Anandkumar, Yisong Yue:
Long-term Forecasting using Tensor-Train RNNs. CoRR abs/1711.00073 (2017) - 2016
- [c2]Stephan Zheng, Yang Song, Thomas Leung, Ian J. Goodfellow:
Improving the Robustness of Deep Neural Networks via Stability Training. CVPR 2016: 4480-4488 - [c1]Stephan Zheng, Yisong Yue, Jennifer A. Hobbs:
Generating Long-term Trajectories Using Deep Hierarchical Networks. NIPS 2016: 1543-1551 - [i1]Stephan Zheng, Yang Song, Thomas Leung, Ian J. Goodfellow:
Improving the Robustness of Deep Neural Networks via Stability Training. CoRR abs/1604.04326 (2016)
Coauthor Index
aka: Alexander R. Trott
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