196 citations · 1.6k across the 143 of their papers we have counts for
7 papers · 1 filter
Complexity of zigzag sampling algorithm for strongly log-concave distributions
Jianfeng Lu, Lihan Wang
We study the computational complexity of zigzag sampling algorithm for strongly log-concave distributions. The zigzag process has the advantage of not requiring time discretization…
Random Coordinate Underdamped Langevin Monte Carlo
Zhiyan Ding, Qin Li, Jianfeng Lu +1
The Underdamped Langevin Monte Carlo (ULMC) is a popular Markov chain Monte Carlo sampling method. It requires the computation of the full gradient of the log-density at each itera…
Random Coordinate Langevin Monte Carlo
Zhiyan Ding, Qin Li, Jianfeng Lu +1
Langevin Monte Carlo (LMC) is a popular Markov chain Monte Carlo sampling method. One drawback is that it requires the computation of the full gradient at each iteration, an expens…
A Mean-field Analysis of Deep ResNet and Beyond: Towards Provable Optimization Via Overparameterization From Depth
Yiping Lu, Chao Ma, Yulong Lu +2
Training deep neural networks with stochastic gradient descent (SGD) can often achieve zero training loss on real-world tasks although the optimization landscape is known to be hig…
Accelerating Langevin Sampling with Birth-death
Yulong Lu, Jianfeng Lu, James Nolen
A fundamental problem in Bayesian inference and statistical machine learning is to efficiently sample from multimodal distributions. Due to metastability, multimodal distributions…
A stochastic version of Stein Variational Gradient Descent for efficient sampling
Lei Li, Yingzhou Li, Jian-Guo Liu +2
We propose in this work RBM-SVGD, a stochastic version of Stein Variational Gradient Descent (SVGD) method for efficiently sampling from a given probability measure and thus useful…
Stochastic modified equations for the asynchronous stochastic gradient descent
Jing An, Jianfeng Lu, Lexing Ying
We propose a stochastic modified equations (SME) for modeling the asynchronous stochastic gradient descent (ASGD) algorithms. The resulting SME of Langevin type extracts more infor…