On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method. - Ye He 003; Krishnakumar Balasubramanian 002; Murat A. Erdogdu (NeurIPS, 2020)

Regularized Stein Variational Gradient Flow. - Ye He 003; Krishnakumar Balasubramanian 002; Bharath K. Sriperumbudur; Jianfeng Lu (CoRR, 2022)

Towards a Complete Analysis of Langevin Monte Carlo: Beyond Poincaré Inequality. - Alireza Mousavi Hosseini; Tyler K. Farghly; Ye He 003; Krishna Balasubramanian; Murat A. Erdogdu (COLT, 2023)

A Separation in Heavy-Tailed Sampling: Gaussian vs. Stable Oracles for Proximal Samplers. - Ye He 003; Alireza Mousavi Hosseini; Krishnakumar Balasubramanian 001; Murat A. Erdogdu (NeurIPS, 2024)

Zeroth-Order Sampling Methods for Non-Log-Concave Distributions: Alleviating Metastability by Denoising Diffusion. - Ye He 003; Kevin Rojas; Molei Tao (NeurIPS, 2024)

Evaluating the design space of diffusion-based generative models. - Yuqing Wang; Ye He 003; Molei Tao (NeurIPS, 2024)

Mean-Square Analysis of Discretized Itô Diffusions for Heavy-tailed Sampling. - Ye He 003; Tyler Farghly; Krishnakumar Balasubramanian 002; Murat A. Erdogdu (J. Mach. Learn. Res., 2024)

An Analysis of Transformed Unadjusted Langevin Algorithm for Heavy-Tailed Sampling. - Ye He 003; Krishnakumar Balasubramanian 002; Murat A. Erdogdu (IEEE Trans. Inf. Theory, 2024)

What Exactly Does Guidance Do in Masked Discrete Diffusion Models. - Ye He 003; Kevin Rojas; Molei Tao (CoRR, 2025)

Theory-Informed Improvements to Classifier-Free Guidance for Discrete Diffusion Models. - Kevin Rojas; Ye He 003; Chieh-Hsin Lai; Yuhta Takida; Yuki Mitsufuji; Molei Tao (CoRR, 2025)