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Jiachen Yang
Jiachen Yang
Simular
Подтвержден адрес электронной почты в домене simular.ai - Главная страница
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Процитировано
Год
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
2002017
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
952019
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
91*2018
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
80*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
742020
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
542020
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
472020
Single Episode Policy Transfer in Reinforcement Learning
J Yang, B Petersen, H Zha, D Faissol
International Conference on Learning Representations, 2019
372019
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
312022
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
292023
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
172021
Graphopt: Learning optimization models of graph formation
R Trivedi, J Yang, H Zha
International Conference on Machine Learning, 9603-9613, 2020
172020
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
162022
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
62022
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
22023
Cooperation in Multi-Agent Reinforcement Learning
J Yang
Georgia Institute of Technology, 2021
12021
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, 112924, 2024
2024
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
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