6 papers
Sample Complexity of Average-Reward Q-Learning: From Single-agent to Federated Reinforcement Learning
Yuchen Jiao, Jiin Woo, Gen Li +2
Average-reward reinforcement learning offers a principled framework for long-term decision-making by maximizing the mean reward per time step. Although Q-learning is a widely used…
Towards a unified framework for guided diffusion models
Yuchen Jiao, Yuxin Chen, Gen Li
Guided or controlled data generation with diffusion models\blfootnote{Partial preliminary results of this work appeared in International Conference on Machine Learning 2025 \citep{…
Optimal Convergence Analysis of DDPM for General Distributions
Yuchen Jiao, Yuchen Zhou, Gen Li
Score-based diffusion models have achieved remarkable empirical success in generating high-quality samples from target data distributions. Among them, the Denoising Diffusion Proba…
Connections between reinforcement learning with feedback,test-time scaling, and diffusion guidance: An anthology
Yuchen Jiao, Yuxin Chen, Gen Li
In this note, we reflect on several fundamental connections among widely used post-training techniques. We clarify some intimate connections and equivalences between reinforcement…
Provable Efficiency of Guidance in Diffusion Models for General Data Distribution
Gen Li, Yuchen Jiao
Diffusion models have emerged as a powerful framework for generative modeling, with guidance techniques playing a crucial role in enhancing sample quality. Despite their empirical…
Minimax-Optimal Multi-Agent Robust Reinforcement Learning
Yuchen Jiao, Gen Li
Multi-agent robust reinforcement learning, also known as multi-player robust Markov games (RMGs), is a crucial framework for modeling competitive interactions under environmental u…