Fake news mitigation via point process based intervention M Farajtabar, J Yang, X Ye, H Xu, R Trivedi, E Khalil, S Li, L Song, H Zha International Conference on Machine Learning, 1097-1106, 2017 | 207 | 2017 |
Learning Deep Mean Field Games for Modeling Large Population Behavior J Yang, X Ye, R Trivedi, H Xu, H Zha International Conference on Learning Representations, 2018 | 101* | 2018 |
Cm3: Cooperative multi-goal multi-stage multi-agent reinforcement learning J Yang, A Nakhaei, D Isele, K Fujimura, H Zha International Conference on Learning Representations, 2019 | 97 | 2019 |
Deep Reinforcement Learning and Simulation as a Path Toward Precision Medicine BK Petersen, J Yang, WS Grathwohl, C Cockrell, C Santiago, G An, ... Journal of Computational Biology 26 (6), 597-604, 2019 | 84* | 2019 |
Hierarchical Cooperative Multi-Agent Reinforcement Learning with Skill Discovery J Yang, I Borovikov, H Zha International Conference on Autonomous Agents and Multi-Agent Systems 19 …, 2020 | 79 | 2020 |
Learning to Incentivize Other Learning Agents J Yang, A Li, M Farajtabar, P Sunehag, E Hughes, H Zha Advances in Neural Information Processing Systems 33, 15208--15219, 2020 | 62 | 2020 |
Integrating independent and centralized multi-agent reinforcement learning for traffic signal network optimization Z Zhang, J Yang, H Zha International Conference on Autonomous Agents and Multi-Agent Systems 19 …, 2020 | 50 | 2020 |
A Unified Framework for Deep Symbolic Regression M Landajuela, CS Lee, J Yang, R Glatt, CP Santiago, I Aravena, ... Advances in Neural Information Processing Systems 35, 33985-33998, 2022 | 42 | 2022 |
Single Episode Policy Transfer in Reinforcement Learning J Yang, B Petersen, H Zha, D Faissol International Conference on Learning Representations, 2019 | 38 | 2019 |
Reinforcement learning for adaptive mesh refinement J Yang, T Dzanic, B Petersen, J Kudo, K Mittal, V Tomov, JS Camier, ... International Conference on Artificial Intelligence and Statistics, 5997-6014, 2023 | 32 | 2023 |
Permutation Invariant Policy Optimization for Mean-Field Multi-Agent Reinforcement Learning: A Principled Approach Y Li, L Wang, J Yang, E Wang, Z Wang, T Zhao, H Zha arXiv preprint arXiv:2105.08268, 2021 | 19 | 2021 |
Adaptive Incentive Design with Multi-Agent Meta-Gradient Reinforcement Learning J Yang, E Wang, R Trivedi, T Zhao, H Zha International Conference on Autonomous Agents and Multi-Agent Systems 21 …, 2022 | 18 | 2022 |
Graphopt: Learning optimization models of graph formation R Trivedi, J Yang, H Zha International Conference on Machine Learning, 9603-9613, 2020 | 18 | 2020 |
Multi-Agent Reinforcement Learning for Adaptive Mesh Refinement J Yang, K Mittal, T Dzanic, S Petrides, B Keith, B Petersen, D Faissol, ... arXiv preprint arXiv:2211.00801, 2022 | 8 | 2022 |
Toward Multi-Fidelity Reinforcement Learning for Symbolic Optimization FL Silva, J Yang, M Landajuela, A Goncalves, A Ladd, D Faissol, ... Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States), 2023 | 2 | 2023 |
DynAMO: Multi-agent reinforcement learning for dynamic anticipatory mesh optimization with applications to hyperbolic conservation laws T Dzanic, K Mittal, D Kim, J Yang, S Petrides, B Keith, R Anderson Journal of Computational Physics 506, 112924, 2024 | 1 | 2024 |
Cooperation in Multi-Agent Reinforcement Learning J Yang Georgia Institute of Technology, 2021 | 1 | 2021 |
DisCo-DSO: Coupling Discrete and Continuous Optimization for Efficient Generative Design in Hybrid Spaces J Pettit, CS Lee, J Yang, A Ho, BK Petersen, M Landajuela | | 2023 |
Multi-fidelity Deep Symbolic Optimization FL da Silva, J Yang, M Landajuela, AR Goncalves, A Ladd, BK Petersen | | 2023 |
Code for Value Decomposition Graph Network and environment for AMR on linear advection J Yang, S Petrides, T Dzanic, K Mittal, R Anderson, B Keith Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States), 2023 | | 2023 |