1 citations · 2 across the 5 of their papers we have counts for
6 papers
Faster Diffusion Models via Higher-Order Approximation
Gen Li, Yuchen Zhou, Yuting Wei +1
In this paper, we explore provable acceleration of diffusion models without any additional retraining. Focusing on the task of approximating a target data distribution in $\mathbb{…
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…
Statistical Inference for Policy Evaluation with Temporal Difference Learning
Weichen Wu, Gen Li, Yuting Wei +1
We investigate the statistical properties of Temporal Difference (TD) learning with Polyak-Ruppert averaging, arguably one of the most widely used algorithms in reinforcement learn…
A Sharp Convergence Theory for The Probability Flow ODEs of Diffusion Models
Gen Li, Yuting Wei, Yuejie Chi +1
Diffusion models, which convert noise into new data instances by learning to reverse a diffusion process, have become a cornerstone in contemporary generative modeling. In this wor…
Towards a mathematical theory for consistency training in diffusion models
Gen Li, Zhihan Huang, Yuting Wei
Consistency models, which were proposed to mitigate the high computational overhead during the sampling phase of diffusion models, facilitate single-step sampling while attaining s…
A non-asymptotic distributional theory of approximate message passing for sparse and robust regression
Gen Li, Yuting Wei
Characterizing the distribution of high-dimensional statistical estimators is a challenging task, due to the breakdown of classical asymptotic theory in high dimension. This paper…