7 citations · 18 across the 5 of their papers we have counts for
5 papers
The Mirror Langevin Algorithm Converges with Vanishing Bias
Ruilin Li, Molei Tao, Santosh S. Vempala +1
The technique of modifying the geometry of a problem from Euclidean to Hessian metric has proved to be quite effective in optimization, and has been the subject of study for sampli…
Sqrt(d) Dimension Dependence of Langevin Monte Carlo
Ruilin Li, Hongyuan Zha, Molei Tao
This article considers the popular MCMC method of unadjusted Langevin Monte Carlo (LMC) and provides a non-asymptotic analysis of its sampling error in 2-Wasserstein distance. The…
Hessian-Free High-Resolution Nesterov Acceleration for Sampling
Ruilin Li, Hongyuan Zha, Molei Tao
Nesterov's Accelerated Gradient (NAG) for optimization has better performance than its continuous time limit (noiseless kinetic Langevin) when a finite step-size is employed \citep…
Improving Sampling Accuracy of Stochastic Gradient MCMC Methods via Non-uniform Subsampling of Gradients
Ruilin Li, Xin Wang, Hongyuan Zha +1
Many Markov Chain Monte Carlo (MCMC) methods leverage gradient information of the potential function of target distribution to explore sample space efficiently. However, computing…
Learning to Match via Inverse Optimal Transport
Ruilin Li, Xiaojing Ye, Haomin Zhou +1
We propose a unified data-driven framework based on inverse optimal transport that can learn adaptive, nonlinear interaction cost function from noisy and incomplete empirical match…