activity
20042026
most citedLong-time behaviors of mean-field interacting particle systems related to McKean-Vlasov equations

35 citations · 161 across the 26 of their papers we have counts for

collaborators

26 papers

cs.LG2026

Non-asymptotic estimates of the minimal risk in statistical learning

Liming Wu, Sen Yang

In this paper we prove some concentration inequalities for two types of error probabilities in the Empirical Risk Principle (ERP) in statistical learning, which provide a lower bou…

math.ST2023

Maximum Entropy Estimator for Hidden Markov Models: Reduction to Dimension 2

Shulan Hu, Xinyu Wang, Liming Wu

In the paper, we introduce the maximum entropy estimator based on 2-dimensional empirical distribution of the observation sequence of hidden Markov model , when the sample size is…

math.PR2020★ 35 cited

Long-time behaviors of mean-field interacting particle systems related to McKean-Vlasov equations

Wei Liu, Liming Wu, Chaoen Zhang

In this paper, we investigate gradient estimate of the Poisson equation and the exponential convergence in the Wasserstein metric , uniform in the number of particle…

math.AP2019★ 9 cited

The Kinetic Fokker-planck Equation With Mean Field Interaction

Arnaud Guillin, Wei Liu, Liming Wu +1

We study the long time behaviour of the kinetic Fokker-Planck equation with mean field interaction, whose limit is often called Vlasov-Fkker-Planck equation. We prove a uniform (in…

math.PR2019

Uniform Poincar{é} and logarithmic Sobolev inequalities for mean field particles systems

Arnaud Guillin, Wei Liu, Liming Wu +1

In this paper we establish some explicit and sharp estimates of the spectral gap and the log-Sobolev constant for mean field particles system, uniform in the number of particles, w…

math.PR2019★ 1 cited

Ricci curvature and -exponential convergence of Markov processes on graphs

Lingyan Cheng, Ruinan Li, Liming Wu

In this paper, we show that the Ricci curvature lower bound in Ollivier's Wasserstein metric sense of a continuous time jumping Markov process on a graph can be characterized by so…