5 citations · 8 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
On Newton Screening
Jian Huang, Yuling Jiao, Lican Kang +4
Screening and working set techniques are important approaches to reducing the size of an optimization problem. They have been widely used in accelerating first-order methods for so…
A Constructive Approach to High-dimensional Regression
Jian Huang, Yuling Jiao, Yanyan Liu +1
We develop a constructive approach to estimating sparse, high-dimensional linear regression models. The approach is a computational algorithm motivated from the KKT conditions for…