| A dual approach to constrained markov decision processes with entropy regularization D Ying, Y Ding, J Lavaei International Conference on Artificial Intelligence and Statistics, 1887-1909, 2022 | 39 | 2022 |
| Provably efficient primal-dual reinforcement learning for CMDPs with non-stationary objectives and constraints Y Ding, J Lavaei AAAI Conference on Artificial Intelligence (AAAI), 2023 | 27 | 2023 |
| Beyond exact gradients: Convergence of stochastic soft-max policy gradient methods with entropy regularization Y Ding, J Zhang, H Lee, J Lavaei arXiv preprint arXiv:2110.10117, 2021 | 23 | 2021 |
| On the Global Optimum Convergence of Momentum-based Policy Gradient Y Ding, J Zhang, J Lavaei International Conference on Artificial Intelligence and Statistics, 1910-1934, 2022 | 21 | 2022 |
| Optimal input design for affine model discrimination with applications in intention-aware vehicles Y Ding, F Harirchi, SZ Yong, E Jacobsen, N Ozay 2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS …, 2018 | 21 | 2018 |
| A CMDP-within-online framework for Meta-Safe Reinforcement Learning MJ Vanshaj Khattar, Yuhao Ding, Bilgehan Sel, Javad Lavaei International Conference on Learning Representations (ICLR), 2023 | 20* | 2023 |
| Input design for nonlinear model discrimination via affine abstraction K Singh, Y Ding, N Ozay, SZ Yong IFAC-PapersOnLine 51 (16), 175-180, 2018 | 19 | 2018 |
| Policy-based Primal-Dual Methods for Convex Constrained Markov Decision Processes D Ying, M Guo, Y Ding, J Lavaei AAAI Conference on Artificial Intelligence (AAAI), 2023 | 15 | 2023 |
| Escaping spurious local minimum trajectories in online time-varying nonconvex optimization Y Ding, J Lavaei, M Arcak 2021 American control conference (ACC), 2021 | 15 | 2021 |
| Scalable Primal-Dual Actor-Critic Method for Safe Multi-Agent RL with General Utilities D Ying, Y Zhang, Y Ding, A Koppel, J Lavaei 37th Annual Conference on Neural Information Processing Systems (NeurIPS 2023), 2023 | 14 | 2023 |
| On the Absence of Spurious Local Trajectories in Time-varying Nonconvex Optimization S Fattahi, C Josz, Y Ding, RM Ghazi, J Lavaei, S Sojoudi IEEE Transactions on Automatic Control, 2021 | 13* | 2021 |
| Time-variation in online nonconvex optimization enables escaping from spurious local minima Y Ding, J Lavaei, M Arcak IEEE Transactions on Automatic Control, 2021 | 9 | 2021 |
| Non-stationary Risk-sensitive Reinforcement Learning: Near-optimal Dynamic Regret, Adaptive Detection, and Separation Design Y Ding, M Jin, J Lavaei AAAI Conference on Artificial Intelligence (AAAI), 2023 | 7 | 2023 |
| Balance reward and safety optimization for safe reinforcement learning: A perspective of gradient manipulation S Gu, B Sel, Y Ding, L Wang, Q Lin, M Jin, A Knoll Proceedings of the AAAI Conference on Artificial Intelligence 38 (19), 21099 …, 2024 | 5 | 2024 |
| Learning-to-Learn to Guide Random Search: Derivative-Free Meta Blackbox Optimization on Manifold B Sel, A Al-Tawaha, Y Ding, R Jia, B Ji, J Lavaei, M Jin Learning for Dynamics & Control Conference (L4DC), 2023 | 4 | 2023 |
| Tempo Adaption in Non-stationary Reinforcement Learning H Lee, Y Ding, J Lee, M Jin, J Lavaei, S Sojoudi 37th Annual Conference on Neural Information Processing Systems (NeurIPS 2023), 2023 | 3 | 2023 |
| Local Analysis of Entropy-Regularized Stochastic Soft-Max Policy Gradient Methods Y Ding, J Zhang, J Lavaei European Control Conference (ECC), 2023 | 3 | 2023 |
| Enhancing Efficiency of Safe Reinforcement Learning via Sample Manipulation S Gu, L Shi, Y Ding, A Knoll, C Spanos, A Wierman, M Jin 38th Annual Conference on Neural Information Processing Systems (NeurIPS 2024）, 2024 | 2 | 2024 |
| Aggressive local search for constrained optimal control problems with many local minima Y Ding, H Feng, J Lavaei arXiv preprint arXiv:1903.08634, 2019 | 2 | 2019 |
| Safe and Balanced: A Framework for Constrained Multi-Objective Reinforcement Learning S Gu, B Sel, Y Ding, L Wang, Q Lin, A Knoll, M Jin IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025 | 1 | 2025 |
Javad LavaeiAssociate Professor, UC BerkeleyΗ διεύθυνση ηλεκτρονικού ταχυδρομείου έχει επαληθευτεί στον τομέα berkeley.edu