5 citations · 5 across the 3 of their papers we have counts for
3 papers
stat.CO2021
Convergence Analysis of Schr{ö}dinger-F{ö}llmer Sampler without Convexity
Yuling Jiao, Lican Kang, Yanyan Liu +1
Schrödinger-Föllmer sampler (SFS) is a novel and efficient approach for sampling from possibly unnormalized distributions without ergodicity. SFS is based on the Euler-Maruyama dis…
stat.CO2021★ 5 cited
Schr{ö}dinger-F{ö}llmer Sampler: Sampling without Ergodicity
Jian Huang, Yuling Jiao, Lican Kang +3
Sampling from probability distributions is an important problem in statistics and machine learning, specially in Bayesian inference when integration with respect to posterior distr…
stat.ML2020
A Support Detection and Root Finding Approach for Learning High-dimensional Generalized Linear Models
Jian Huang, Yuling Jiao, Lican Kang +3
Feature selection is important for modeling high-dimensional data, where the number of variables can be much larger than the sample size. In this paper, we develop a support detect…