15 citations · 20 across the 10 of their papers we have counts for
10 papers
Model Free Prediction with Uncertainty Assessment
Yuling Jiao, Lican Kang, Jin Liu +2
Deep nonparametric regression, characterized by the utilization of deep neural networks to learn target functions, has emerged as a focus of research attention in recent years. Des…
Latent Schr{ö}dinger Bridge Diffusion Model for Generative Learning
Yuling Jiao, Lican Kang, Huazhen Lin +2
This paper aims to conduct a comprehensive theoretical analysis of current diffusion models. We introduce a novel generative learning methodology utilizing the Schr{ö}dinger bridge…
Lipschitz Transport Maps via the Follmer Flow
Yin Dai, Yuan Gao, Jian Huang +3
Inspired by the construction of the F{ö}llmer process, we construct a unit-time flow on the Euclidean space, termed the F{ö}llmer flow, whose flow map at time 1 pushes forward a st…
An empirical study of the effect of background data size on the stability of SHapley Additive exPlanations (SHAP) for deep learning models
Han Yuan, Mingxuan Liu, Lican Kang +2
Nowadays, the interpretation of why a machine learning (ML) model makes certain inferences is as crucial as the accuracy of such inferences. Some ML models like the decision tree p…
A Data-Driven Line Search Rule for Support Recovery in High-dimensional Data Analysis
Peili Li, Yuling Jiao, Xiliang Lu +1
In this work, we consider the algorithm to the (nonlinear) regression problems with penalty. The existing algorithms for based optimization problem are often carr…
Global Optimization via Schr{ö}dinger-F{ö}llmer Diffusion
Yin Dai, Yuling Jiao, Lican Kang +2
We study the problem of finding global minimizers of approximately via sampling from a probability distribution with density $p_σ(x)=…
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…