7 papers
Randomized Midpoint Method for Log-Concave Sampling under Constraints
Yifeng Yu, Shijie Zhang, Lu Yu
In this paper, we study the problem of sampling from log-concave distributions supported on convex and compact sets, with a particular focus on the randomized midpoint discretizati…
On the Limits of Latent Reuse in Diffusion Models
Yifeng Yu, Lu Yu
Diffusion models are often trained in low-dimensional latent spaces, which are then reused for related but shifted datasets. In this work, we study when such latent reuse remains r…
Kinetic Langevin Splitting Schemes for Constrained Sampling
Neil K. Chada, Lu Yu
Constrained sampling is an important and challenging task in computational statistics, concerned with generating samples from a distribution under certain constraints. There are nu…
Know What You Know: Metacognitive Entropy Calibration for Verifiable RL Reasoning
Qiannian Zhao, Chen Yang, Jinhao Jing +5
Large reasoning models (LRMs) have emerged as a powerful paradigm for solving complex real-world tasks. In practice, these models are predominantly trained via Reinforcement Learni…
Diffusion Models with Heavy-Tailed Targets: Score Estimation and Sampling Guarantees
Yifeng Yu, Lu Yu
Score-based diffusion models have become a powerful framework for generative modeling, with score estimation as a central statistical bottleneck. Existing guarantees for score esti…
Advancing Wasserstein Convergence Analysis of Score-Based Models: Insights from Discretization and Second-Order Acceleration
Yifeng Yu, Lu Yu
Score-based diffusion models have emerged as powerful tools in generative modeling, yet their theoretical foundations remain underexplored. In this work, we focus on the Wasserstei…