He, Ye

9 publications

ICLR 2026 Improving Classifier-Free Guidance in Masked Diffusion: Low-Dim Theoretical Insights with High-Dim Impact Kevin Rojas, Ye He, Chieh-Hsin Lai, Yuhta Takida, Yuki Mitsufuji, Molei Tao

ICLR 2026 Wavelet Predictive Representations for Non-Stationary Reinforcement Learning Min Wang, Xin Li, Ye He, Yao-Hui Li, Hasnaa Bennis, Riashat Islam, Mingzhong Wang

ICLR 2026 What Exactly Does Guidance Do in Masked Discrete Diffusion Models Ye He, Kevin Rojas, Molei Tao

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

NeurIPS 2024 Evaluating the Design Space of Diffusion-Based Generative Models Yuqing Wang, Ye He, Molei Tao

JMLR 2024 Mean-Square Analysis of Discretized Itô Diffusions for Heavy-Tailed Sampling Ye He, Tyler Farghly, Krishnakumar Balasubramanian, Murat A. Erdogdu

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

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

NeurIPS 2020 On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method Ye He, Krishnakumar Balasubramanian, Murat A Erdogdu