2 citations · 2 across the 7 of their papers we have counts for
5 papers · 1 filter
Newton Matching for Generative Modeling: A Unified Framework for Fine-Tuning and Sampling
Zeyang Li, Yunan Wang, Paolo Giaretta +1
We develop Newton Matching, a unified framework for fine-tuning and sampling in generative modeling. The target is , where is the reward, the inverse tem…
DiRecT: Safe Diffusion-Based Planning via Receding-Horizon Denoising
Paolo Giaretta, Zeyang Li, Navid Azizan
Diffusion models have emerged as powerful tools for planning and control by learning multimodal distributions over actions and trajectories. Yet reliable inference-time safety enfo…
Robust Safe Reinforcement Learning under Adversarial Disturbances
Zeyang Li, Chuxiong Hu, Shengbo Eben Li +2
Safety is a primary concern when applying reinforcement learning to real-world control tasks, especially in the presence of external disturbances. However, existing safe reinforcem…
Bridging the Gap between Newton-Raphson Method and Regularized Policy Iteration
Zeyang Li, Chuxiong Hu, Yunan Wang +4
Regularization is a cornerstone of modern reinforcement learning. Regularized policy iteration (RPI) provides a fundamental scheme for solving regularized Markov decision processes…
Safe Reinforcement Learning with Dual Robustness
Zeyang Li, Chuxiong Hu, Yunan Wang +2
Reinforcement learning (RL) agents are vulnerable to adversarial disturbances, which can deteriorate task performance or compromise safety specifications. Existing methods either a…