Search Results for author: Kun Chen

Found 17 papers, 3 papers with code

Global Convergence Analysis of Deep Linear Networks with A One-neuron Layer

no code implementations8 Jan 2022 Kun Chen, Dachao Lin, Zhihua Zhang

In this paper, we follow Eftekhari's work to give a non-local convergence analysis of deep linear networks.

Correcting the User Feedback-Loop Bias for Recommendation Systems

no code implementations13 Sep 2021 Weishen Pan, Sen Cui, Hongyi Wen, Kun Chen, ChangShui Zhang, Fei Wang

We empirically validated the existence of such user feedback-loop bias in real world recommendation systems and compared the performance of our method with the baseline models that are either without de-biasing or with propensity scores estimated by other methods.

Recommendation Systems Selection bias

Learning to Collaborate

no code implementations18 Aug 2021 Sen Cui, Jian Liang, Weishen Pan, Kun Chen, ChangShui Zhang, Fei Wang

We propose a learning to collaborate framework, where each client can choose to collaborate with certain members in the network to achieve a "collaboration equilibrium", where smaller collaboration coalitions are formed within the network so that each client can obtain the model with the best utility.

Robust Finite Mixture Regression for Heterogeneous Targets

no code implementations12 Oct 2020 Jian Liang, Kun Chen, Ming Lin, ChangShui Zhang, Fei Wang

FMR is an effective scheme for handling sample heterogeneity, where a single regression model is not enough for capturing the complexities of the conditional distribution of the observed samples given the features.

Suicide Risk Modeling with Uncertain Diagnostic Records

no code implementations5 Sep 2020 Wenjie Wang, Chongliang Luo, Robert H. Aseltine, Fei Wang, Jun Yan, Kun Chen

Motivated by the pressing need for suicide prevention through improving behavioral healthcare, we use medical claims data to study the risk of subsequent suicide attempts for patients who were hospitalized due to suicide attempts and later discharged.

Survival Analysis

Statistically Guided Divide-and-Conquer for Sparse Factorization of Large Matrix

no code implementations17 Mar 2020 Kun Chen, Ruipeng Dong, Wanwan Xu, Zemin Zheng

In the first stage of division, we consider both sequential and parallel approaches for simplifying the task into a set of co-sparse unit-rank estimation (CURE) problems, and establish the statistical underpinnings of these commonly-adopted and yet poorly understood deflation methods.

Time Series

Multivariate Functional Regression via Nested Reduced-Rank Regularization

no code implementations10 Mar 2020 Xiaokang Liu, Shujie Ma, Kun Chen

We propose a nested reduced-rank regression (NRRR) approach in fitting regression model with multivariate functional responses and predictors, to achieve tailored dimension reduction and facilitate interpretation/visualization of the resulting functional model.

Dimensionality Reduction

Pursuing Sources of Heterogeneity in Modeling Clustered Population

no code implementations10 Mar 2020 Yan Li, Chun Yu, Yize Zhao, Robert H. Aseltine, Weixin Yao, Kun Chen

We clarify the concepts of the source of heterogeneity that account for potential scale differences of the clusters and propose a regularized finite mixture effects regression to achieve heterogeneity pursuit and feature selection simultaneously.

Impact of Temperature and Relative Humidity on the Transmission of COVID-19: A Modeling Study in China and the United States

no code implementations9 Mar 2020 Jingyuan Wang, Ke Tang, Kai Feng, Xin Li, Weifeng Lv, Kun Chen, Fei Wang

Primary outcome measures: Regression analysis of the impact of temperature and relative humidity on the effective reproductive number (R value).

Communication-Efficient Distributed SVD via Local Power Iterations

no code implementations19 Feb 2020 Xiang Li, Shusen Wang, Kun Chen, Zhihua Zhang

As a practical surrogate of OPT, sign-fixing, which uses a diagonal matrix with $\pm 1$ entries as weights, has better computation complexity and stability in experiments.

Distributed Computing

Boosted Sparse and Low-Rank Tensor Regression

2 code implementations NeurIPS 2018 Lifang He, Kun Chen, Wanwan Xu, Jiayu Zhou, Fei Wang

We propose a sparse and low-rank tensor regression model to relate a univariate outcome to a feature tensor, in which each unit-rank tensor from the CP decomposition of the coefficient tensor is assumed to be sparse.

Integrative Multi-View Reduced-Rank Regression: Bridging Group-Sparse and Low-Rank Models

1 code implementation26 Jul 2018 Gen Li, Xiaokang Liu, Kun Chen

Multi-view data have been routinely collected in various fields of science and engineering.

MULTI-VIEW LEARNING

Multi-View Graph Convolutional Network and Its Applications on Neuroimage Analysis for Parkinson's Disease

1 code implementation22 May 2018 Xi Sheryl Zhang, Lifang He, Kun Chen, Yuan Luo, Jiayu Zhou, Fei Wang

Parkinson's Disease (PD) is one of the most prevalent neurodegenerative diseases that affects tens of millions of Americans.

Beyond the Click-Through Rate: Web Link Selection with Multi-level Feedback

no code implementations4 May 2018 Kun Chen, Kechao Cai, Longbo Huang, John C. S. Lui

The web link selection problem is to select a small subset of web links from a large web link pool, and to place the selected links on a web page that can only accommodate a limited number of links, e. g., advertisements, recommendations, or news feeds.

Multi-level Feedback Web Links Selection Problem: Learning and Optimization

no code implementations8 Sep 2017 Kechao Cai, Kun Chen, Longbo Huang, John C. S. Lui

To our best knowledge, we are the first to model the links selection problem as a constrained multi-armed bandit problem and design an effective links selection algorithm by learning the links' multi-level structure with provable \emph{sub-linear} regret and violation bounds.

SOFAR: large-scale association network learning

no code implementations26 Apr 2017 Yoshimasa Uematsu, Yingying Fan, Kun Chen, Jinchi Lv, Wei. Lin

Many modern big data applications feature large scale in both numbers of responses and predictors.

Regularization vs. Relaxation: A conic optimization perspective of statistical variable selection

no code implementations20 Oct 2015 Hongbo Dong, Kun Chen, Jeff Linderoth

In particular, we show that a popular sparsity-inducing concave penalty function known as the Minimax Concave Penalty (MCP), and the reverse Huber penalty derived in a recent work by Pilanci, Wainwright and Ghaoui, can both be derived as special cases of a lifted convex relaxation called the perspective relaxation.

Combinatorial Optimization Variable Selection

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