5 citations · 22 across the 10 of their papers we have counts for
10 papers
Inference on High-dimensional Single-index Models with Streaming Data
Dongxiao Han, Jinhan Xie, Jin Liu +4
Traditional statistical methods are faced with new challenges due to streaming data. The major challenge is the rapidly growing volume and velocity of data, which makes storing suc…
A Deep Generative Approach to Conditional Sampling
Xingyu Zhou, Yuling Jiao, Jin Liu +1
We propose a deep generative approach to sampling from a conditional distribution based on a unified formulation of conditional distribution and generalized nonparametric regressio…
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…
Generative Learning With Euler Particle Transport
Yuan Gao, Jian Huang, Yuling Jiao +3
We propose an Euler particle transport (EPT) approach for generative learning. The proposed approach is motivated by the problem of finding an optimal transport map from a referenc…
Deep Dimension Reduction for Supervised Representation Learning
Jian Huang, Yuling Jiao, Xu Liao +2
The goal of supervised representation learning is to construct effective data representations for prediction. Among all the characteristics of an ideal nonparametric representation…
Learning Implicit Generative Models with Theoretical Guarantees
Yuan Gao, Jian Huang, Yuling Jiao +1
We propose a \textbf{uni}fied \textbf{f}ramework for \textbf{i}mplicit \textbf{ge}nerative \textbf{m}odeling (UnifiGem) with theoretical guarantees by integrating approaches from o…
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…
BIVAS: A scalable Bayesian method for bi-level variable selection with applications
Mingxuan Cai, Mingwei Dai, Jingsi Ming +3
In this paper, we consider a Bayesian bi-level variable selection problem in high-dimensional regressions. In many practical situations, it is natural to assign group membership to…
A Unified Primal Dual Active Set Algorithm for Nonconvex Sparse Recovery
Jian Huang, Yuling Jiao, Bangti Jin +3
In this paper, we consider the problem of recovering a sparse signal based on penalized least squares formulations. We develop a novel algorithm of primal-dual active set type for…