5 citations · 8 across the 5 of their papers we have counts for
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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.CO2017★ 3 cited
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…