2 citations · 4 across the 7 of their papers we have counts for
7 papers
Proximal Oracles for Optimization and Sampling
Jiaming Liang, Yongxin Chen
We consider convex optimization with non-smooth objective function and log-concave sampling with non-smooth potential (negative log density). In particular, we study two specific s…
On a Class of Gibbs Sampling over Networks
Bo Yuan, Jiaojiao Fan, Jiaming Liang +2
We consider the sampling problem from a composite distribution whose potential (negative log density) is $\sum_{i=1}^n f_i(x_i)+\sum_{j=1}^m g_j(y_j)+\sum_{i=1}^n\sum_{j=1}^m\frac{…
Improved dimension dependence of a proximal algorithm for sampling
Jiaojiao Fan, Bo Yuan, Yongxin Chen
We propose a sampling algorithm that achieves superior complexity bounds in all the classical settings (strongly log-concave, log-concave, Logarithmic-Sobolev inequality (LSI), Poi…
Nesterov smoothing for sampling without smoothness
Jiaojiao Fan, Bo Yuan, Jiaming Liang +1
We study the problem of sampling from a target distribution in whose potential is not smooth. Compared with the sampling problem with smooth potentials, this problem…
A Proximal Algorithm for Sampling from Non-convex Potentials
Jiaming Liang, Yongxin Chen
We study sampling problems associated with non-convex potentials that meanwhile lack smoothness. In particular, we consider target distributions that satisfy either logarithmic-Sob…
A Proximal Algorithm for Sampling
Jiaming Liang, Yongxin Chen
We study sampling problems associated with potentials that lack smoothness. The potentials can be either convex or non-convex. Departing from the standard smooth setting, the poten…
A Proximal Algorithm for Sampling from Non-smooth Potentials
Jiaming Liang, Yongxin Chen
In this work, we examine sampling problems with non-smooth potentials. We propose a novel Markov chain Monte Carlo algorithm for sampling from non-smooth potentials. We provide a n…