14 citations · 18 across the 10 of their papers we have counts for
13 papers
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…
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…
Parallelized Midpoint Randomization for Langevin Monte Carlo
Lu Yu, Arnak Dalalyan
We study the problem of sampling from a target probability density function in frameworks where parallel evaluations of the log-density gradient are feasible. Focusing on smooth an…
Langevin Monte Carlo for strongly log-concave distributions: Randomized midpoint revisited
Lu Yu, Avetik Karagulyan, Arnak Dalalyan
We revisit the problem of sampling from a target distribution that has a smooth strongly log-concave density everywhere in . In this context, if no additional density…
Mirror Descent Strikes Again: Optimal Stochastic Convex Optimization under Infinite Noise Variance
Nuri Mert Vural, Lu Yu, Krishnakumar Balasubramanian +2
We study stochastic convex optimization under infinite noise variance. Specifically, when the stochastic gradient is unbiased and has uniformly bounded -th moment, for some…
Spectral Clustering with Variance Information for Group Structure Estimation in Panel Data
Lu Yu, Jiaying Gu, Stanislav Volgushev
Consider a panel data setting where repeated observations on individuals are available. Often it is reasonable to assume that there exist groups of individuals that share similar e…
An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Bias
Lu Yu, Krishnakumar Balasubramanian, Stanislav Volgushev +1
Structured non-convex learning problems, for which critical points have favorable statistical properties, arise frequently in statistical machine learning. Algorithmic convergence…
False Discovery Rates in Biological Networks
Lu Yu, Tobias Kaufmann, Johannes Lederer
The increasing availability of data has generated unprecedented prospects for network analyses in many biological fields, such as neuroscience (e.g., brain networks), genomics (e.g…
Oracle Inequalities for High-dimensional Prediction
Johannes Lederer, Lu Yu, Irina Gaynanova
The abundance of high-dimensional data in the modern sciences has generated tremendous interest in penalized estimators such as the lasso, scaled lasso, square-root lasso, elastic…