| The ai economist: Improving equality and productivity with ai-driven tax policies S Zheng, A Trott, S Srinivasa, N Naik, M Gruesbeck, DC Parkes, R Socher arXiv preprint arXiv:2004.13332, 2020 | 167 | 2020 |
| Explore, discover and learn: Unsupervised discovery of state-covering skills V Campos, A Trott, C Xiong, R Socher, X Giró-i-Nieto, J Torres International Conference on Machine Learning, 1317-1327, 2020 | 137 | 2020 |
| The AI Economist: Taxation policy design via two-level deep multiagent reinforcement learning S Zheng, A Trott, S Srinivasa, DC Parkes, R Socher Science advances 8 (18), eabk2607, 2022 | 121 | 2022 |
| Keeping your distance: Solving sparse reward tasks using self-balancing shaped rewards A Trott, S Zheng, C Xiong, R Socher Advances in Neural Information Processing Systems 32, 2019 | 109 | 2019 |
| Interpretable counting in visual question answering AR Trott, C Xiong, R Socher US Patent 10,592,767, 2020 | 94 | 2020 |
| Interpretable counting for visual question answering A Trott, C Xiong, R Socher arXiv preprint arXiv:1712.08697, 2017 | 68 | 2017 |
| Competitive experience replay H Liu, A Trott, R Socher, C Xiong arXiv preprint arXiv:1902.00528, 2019 | 63 | 2019 |
| Input-gain control produces feature-specific surround suppression AR Trott, RT Born Journal of Neuroscience 35 (12), 4973-4982, 2015 | 32 | 2015 |
| Song Choice Is Modulated by Female Movement in Drosophila Males AR Trott, NC Donelson, LC Griffith, A Ejima Public Library of Science 7 (9), e46025, 2012 | 32 | 2012 |
| Building a foundation for data-driven, interpretable, and robust policy design using the ai economist A Trott, S Srinivasa, D van der Wal, S Haneuse, S Zheng arXiv preprint arXiv:2108.02904, 2021 | 30 | 2021 |
| Cortical magnification plus cortical plasticity equals vision? RT Born, AR Trott, TS Hartmann Vision research 111, 161-169, 2015 | 29 | 2015 |
| Learning world graphs to accelerate hierarchical reinforcement learning W Shang, A Trott, S Zheng, C Xiong, R Socher arXiv preprint arXiv:1907.00664, 2019 | 24 | 2019 |
| Platform behavior under market shocks: A simulation framework and reinforcement-learning based study X Wang, GQ Ma, A Eden, C Li, A Trott, S Zheng, D Parkes Proceedings of the ACM Web Conference 2023, 3592-3602, 2023 | 8 | 2023 |
| Limit: Less is more for instruction tuning across evaluation paradigms A Jha, S Havens, J Dohmann, A Trott, J Portes arXiv preprint arXiv:2311.13133, 2023 | 6 | 2023 |
| Mosaicbert: How to train bert with a lunch money budget J Portes, AR Trott, S Havens, D King, A Venigalla, M Nadeem, N Sardana, ... Workshop on Efficient Systems for Foundation Models@ ICML2023, 2023 | 6 | 2023 |
| Explore, discover and learn: unsupervised discovery of state-covering skills V Campos Camúñez, A Trott, C Xiong, R Socher, X Giró Nieto, ... ICML 2020, Thirty-seventh International Conference on Machine Learning …, 2020 | 6 | 2020 |
| Finding general equilibria in many-agent economic simulations using deep reinforcement learning M Curry, AR Trott, S Phade, Y Bai, S Zheng | 5 | 2022 |
| Learning Solutions in Large Economic Networks using Deep Multi-Agent Reinforcement Learning. M Curry, A Trott, S Phade, Y Bai, S Zheng AAMAS, 2760-2762, 2023 | 4 | 2023 |
| Analyzing Micro-Founded General Equilibrium Models with Many Agents using Deep Reinforcement Learning M Curry, A Trott, S Phade, Y Bai, S Zheng arXiv preprint arXiv:2201.01163, 2022 | 3 | 2022 |
| Modeling Bounded Rationality in Multi-Agent Simulations Using Rationally Inattentive Reinforcement Learning T Mu, S Zheng, AR Trott Transactions on Machine Learning Research, 2022 | 2 | 2022 |
