7 papers
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…
Transformers Meet In-Context Learning: A Universal Approximation Theory
Gen Li, Yuchen Jiao, Yu Huang +2
Large language models are capable of in-context learning, the ability to perform new tasks at test time using a handful of input-output examples, without parameter updates. We deve…
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…
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…