4 citations · 7 across the 11 of their papers we have counts for
11 papers
Nonparametric learning of heterogeneous graphical model on network-linked data
Yuwen Wang, Changyu Liu, Xin He +1
Graphical models have been popularly used for capturing conditional independence structure in multivariate data, which are often built upon independent and identically distributed…
Growing the Efficient Frontier on Panel Trees
Lin William Cong, Guanhao Feng, Jingyu He +1
We introduce a new class of tree-based models, P-Trees, for analyzing (unbalanced) panel of individual asset returns, generalizing high-dimensional sorting with economic guidance a…
Implicit Regularization in Over-Parameterized Support Vector Machine
Yang Sui, Xin He, Yang Bai
In this paper, we design a regularization-free algorithm for high-dimensional support vector machines (SVMs) by integrating over-parameterization with Nesterov's smoothing method,…
Structural transfer learning of non-Gaussian DAG
Mingyang Ren, Xin He, Junhui Wang
Directed acyclic graph (DAG) has been widely employed to represent directional relationships among a set of collected nodes. Yet, the available data in one single study is often li…
A Sharp Gaussian Tail Bound for Sums of Uniforms
Xinjie He, Tomasz Tkocz, Katarzyna Wyczesany
We prove that the tail probabilities of sums of independent uniform random variables, up to a multiplicative constant, are dominated by the Gaussian tail with matching variance and…
Nonparametric augmented probability weighting with sparsity
Xin He, Xiaojun Mao, Zhonglei Wang
Nonresponse frequently arises in practice, and simply ignoring it may lead to erroneous inference. Besides, the number of collected covariates may increase as the sample size in mo…
Efficient Learning of Quadratic Variance Function Directed Acyclic Graphs via Topological Layers
Wei Zhou, Xin He, Wei Zhong +1
Directed acyclic graph (DAG) models are widely used to represent causal relationships among random variables in many application domains. This paper studies a special class of non-…
Learning linear non-Gaussian directed acyclic graph with diverging number of nodes
Ruixuan Zhao, Xin He, Junhui Wang
Acyclic model, often depicted as a directed acyclic graph (DAG), has been widely employed to represent directional causal relations among collected nodes. In this article, we propo…
Kernel-based estimation for partially functional linear model: Minimax rates and randomized sketches
Shaogao Lv, Xin He, Junhui Wang
This paper considers the partially functional linear model (PFLM) where all predictive features consist of a functional covariate and a high dimensional scalar vector. Over an infi…
Structure learning via unstructured kernel-based M-regression
Xin He, Yeheng Ge, Xingdong Feng
In statistical learning, identifying underlying structures of true target functions based on observed data plays a crucial role to facilitate subsequent modeling and analysis. Unli…
Efficient kernel-based variable selection with sparsistency
Xin He, Junhui Wang, Shaogao Lv
Variable selection is central to high-dimensional data analysis, and various algorithms have been developed. Ideally, a variable selection algorithm shall be flexible, scalable, an…