8 papers
Diffusion Models Adapt to Low-Dimensional Structure Under Flexible Coefficient Choices
Changxiao Cai, Yuchen Jiao, Gen Li
Diffusion models are known to exploit unknown low-dimensional structure to accelerate sampling. However, existing convergence theory under low-dimensional data structure has largel…
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…
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…
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{…
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…
Instance-dependent Convergence Theory for Diffusion Models
Yuchen Jiao, Gen Li
Score-based diffusion models have demonstrated outstanding empirical performance in machine learning and artificial intelligence, particularly in generating high-quality new sample…