no code implementations • 16 Oct 2023 • Mingyang Ren, Xin He, Junhui Wang
Directed acyclic graph (DAG) has been widely employed to represent directional relationships among a set of collected nodes.
no code implementations • 2 Sep 2023 • Changyu Liu, Yuling Jiao, Junhui Wang, Jian Huang
For the quadratic loss in nonparametric regression, we show that the adversarial excess risk bound can be improved over those for a general loss.
no code implementations • 17 Nov 2022 • Mingyang Ren, Yaoming Zhen, Junhui Wang
Tensor Gaussian graphical models (GGMs), interpreting conditional independence structures within tensor data, have important applications in numerous areas.
no code implementations • 15 Nov 2022 • Haoran Zhang, Junhui Wang
Longitudinal network consists of a sequence of temporal edges among multiple nodes, where the temporal edges are observed in real time.
no code implementations • 8 Jul 2022 • Haoran Zhang, Junhui Wang
This paper develops a unified embedding model for signed networks to disentangle the intertwined balance structure and anomaly effect, which can greatly facilitate the downstream analysis, including community detection, anomaly detection, and network inference.
no code implementations • 14 Apr 2022 • Huiling Yuan, Guodong Li, Junhui Wang
This paper introduces one new multivariate volatility model that can accommodate an appropriately defined network structure based on low-frequency and high-frequency data.
no code implementations • NeurIPS 2021 • Shaogao Lv, Junhui Wang, Jiankun Liu, Yong liu
In this paper, we provide theoretical results of estimation bounds and excess risk upper bounds for support vector machine (SVM) with sparse multi-kernel representation.
no code implementations • 1 Nov 2021 • Wei Zhou, Xin He, Wei Zhong, Junhui Wang
Directed acyclic graph (DAG) models are widely used to represent causal relationships among random variables in many application domains.
no code implementations • 1 Nov 2021 • Ruixuan Zhao, Xin He, Junhui Wang
The proposed method leverages a novel concept of topological layer to facilitate the DAG learning.
no code implementations • 18 Oct 2021 • 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.
no code implementations • 29 Sep 2021 • SHIRONG XU, Junhui Wang
Recommender system is capable of predicting preferred items for a user by integrating information from similar users or items.
no code implementations • 28 Mar 2021 • Yaoming Zhen, Junhui Wang
Conventional network data has largely focused on pairwise interactions between two entities, yet multi-way interactions among multiple entities have been frequently observed in real-life hypergraph networks.
no code implementations • 21 Jul 2020 • Long Feng, Junhui Wang
For image data related matrix recovery problems, approximate low-rankness and smoothness are the two most commonly imposed structures.
no code implementations • 26 Feb 2018 • Xin He, Junhui Wang, Shaogao Lv
Variable selection is central to high-dimensional data analysis, and various algorithms have been developed.
no code implementations • 19 Jun 2013 • Junhui Wang
In this paper, we propose a high- dimensional multivariate conditional regression model for constructing sparse estimates of the multivariate regression coefficient matrix that accounts for the dependency struc- ture among the multiple responses.
no code implementations • 22 Sep 2012 • Tu Xu, Junhui Wang
Conventional multiclass conditional probability estimation methods, such as Fisher's discriminate analysis and logistic regression, often require restrictive distributional model assumption.
no code implementations • 16 Aug 2012 • Wei Sun, Junhui Wang, Yixin Fang
The key idea is to select the tuning parameters so that the resultant penalized regression model is stable in variable selection.