| Improving the robustness of deep neural networks via stability training S Zheng, Y Song, T Leung, I Goodfellow Proceedings of the ieee conference on computer vision and pattern …, 2016 | 715 | 2016 |
| 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 | 156 | 2020 |
| Long-term forecasting using tensor-train rnns R Yu, S Zheng, A Anandkumar, Y Yue | 156 | 2018 |
| Naomi: Non-autoregressive multiresolution sequence imputation Y Liu, R Yu, S Zheng, E Zhan, Y Yue Advances in neural information processing systems 32, 2019 | 131 | 2019 |
| Novel deep learning methods for track reconstruction S Farrell, P Calafiura, M Mudigonda, D Anderson, JR Vlimant, S Zheng, ... arXiv preprint arXiv:1810.06111, 2018 | 121 | 2018 |
| Robustness gym: Unifying the NLP evaluation landscape K Goel, N Rajani, J Vig, S Tan, J Wu, S Zheng, C Xiong, M Bansal, C Ré arXiv preprint arXiv:2101.04840, 2021 | 120 | 2021 |
| Generating long-term trajectories using deep hierarchical networks S Zheng, Y Yue, J Hobbs Advances in Neural Information Processing Systems 29, 2016 | 108 | 2016 |
| 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 | 103* | 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 | 101 | 2019 |
| Simulation intelligence: Towards a new generation of scientific methods A Lavin, D Krakauer, H Zenil, J Gottschlich, T Mattson, J Brehmer, ... arXiv preprint arXiv:2112.03235, 2021 | 95 | 2021 |
| Technology readiness levels for machine learning systems A Lavin, CM Gilligan-Lee, A Visnjic, S Ganju, D Newman, S Ganguly, ... Nature Communications 13 (1), 6039, 2022 | 83 | 2022 |
| Generating multi-agent trajectories using programmatic weak supervision E Zhan, S Zheng, Y Yue, L Sha, P Lucey arXiv preprint arXiv:1803.07612, 2018 | 77 | 2018 |
| Long-term forecasting using higher order tensor RNNs R Yu, S Zheng, A Anandkumar, Y Yue arXiv preprint arXiv:1711.00073, 2017 | 65 | 2017 |
| The HEP. TrkX Project: deep neural networks for HL-LHC online and offline tracking S Farrell, D Anderson, P Calafiura, G Cerati, L Gray, J Kowalkowski, ... EPJ Web of Conferences 150, 00003, 2017 | 58 | 2017 |
| On the generalization gap in reparameterizable reinforcement learning H Wang, S Zheng, C Xiong, R Socher International Conference on Machine Learning, 6648-6658, 2019 | 35 | 2019 |
| Generative Multi-Agent Behavioral Cloning E Zhan, S Zheng, Y Yue, P Lucey arXiv preprint arXiv:1803.07612, 2018 | 35 | 2018 |
| The HEP. TrkX project: deep learning for particle tracking A Tsaris, D Anderson, J Bendavid, P Calafiura, G Cerati, J Esseiva, ... Journal of Physics: Conference Series 1085, 042023, 2018 | 26 | 2018 |
| Fine-grained retrieval of sports plays using tree-based alignment of trajectories L Sha, P Lucey, S Zheng, T Kim, Y Yue, S Sridharan arXiv preprint arXiv:1710.02255, 2017 | 25 | 2017 |
| Learning world graphs to accelerate hierarchical reinforcement learning W Shang, A Trott, S Zheng, C Xiong, R Socher arXiv preprint arXiv:1907.00664, 2019 | 23 | 2019 |
| 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 | 21 | 2021 |
Yisong YueCalifornia Institute of Technology; Latitude AIVerified email at caltech.edu
