no code implementations • 8 Oct 2024 • Junchao Gong, Siwei Tu, Weidong Yang, Ben Fei, Kun Chen, Wenlong Zhang, Xiaokang Yang, Wanli Ouyang, Lei Bai
By rethinking the blurriness in precipitation nowcasting as a blur kernel acting on predictions, we propose an unsupervised postprocessing method to eliminate the blurriness without the requirement of training with the pairs of blurry predictions and corresponding ground truth.
no code implementations • 3 Jun 2024 • Kun Chen, Tao Chen, Peng Ye, Hao Chen, Kang Chen, Tao Han, Wanli Ouyang, Lei Bai
Data assimilation is a vital component in modern global medium-range weather forecasting systems to obtain the best estimation of the atmospheric state by combining the short-term forecast and observations.
no code implementations • 28 Feb 2024 • Pengcheng Hou, Tao Wang, Daniel Cerkoney, Xiansheng Cai, Zhiyi Li, Youjin Deng, Lei Wang, Kun Chen
We propose a computational graph representation of high-order Feynman diagrams in Quantum Field Theory (QFT), applicable to any combination of spatial, temporal, momentum, and frequency domains.
1 code implementation • 18 Dec 2023 • Kun Chen, Lei Bai, Fenghua Ling, Peng Ye, Tao Chen, Jing-Jia Luo, Hao Chen, Yi Xiao, Kang Chen, Tao Han, Wanli Ouyang
Initial states are typically generated by traditional data assimilation components, which are computational expensive and time-consuming.
1 code implementation • 11 Dec 2022 • Avijit Mitra, Richeek Pradhan, Rachel D Melamed, Kun Chen, David C Hoaglin, Katherine L Tucker, Joel I Reisman, Zhichao Yang, Weisong Liu, Jack Tsai, Hong Yu
All SDOH, measured by structured data and NLP, were significantly associated with increased risk of suicide.
no code implementations • 18 Jun 2022 • Jianmin Chen, Robert H. Aseltine, Fei Wang, Kun Chen
In a suicide risk study with EHR data, our approach is able to select and aggregate prior mental health diagnoses as guided by the diagnosis hierarchy of the International Classification of Diseases.
no code implementations • 8 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.
no code implementations • 13 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.
1 code implementation • 18 Aug 2021 • Sen Cui, Jian Liang, Weishen Pan, Kun Chen, ChangShui Zhang, Fei Wang
Federated learning (FL) refers to the paradigm of learning models over a collaborative research network involving multiple clients without sacrificing privacy.
no code implementations • 12 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.
no code implementations • 5 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.
no code implementations • 17 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.
no code implementations • 10 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.
no code implementations • 10 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.
no code implementations • 9 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).
1 code implementation • 19 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.
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.
1 code implementation • 26 Jul 2018 • Gen Li, Xiaokang Liu, Kun Chen
Multi-view data have been routinely collected in various fields of science and engineering.
1 code implementation • 22 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.
no code implementations • 4 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.
no code implementations • 8 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.
no code implementations • 26 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.
no code implementations • 20 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.