Search Results for author: Kun Chen

Found 23 papers, 7 papers with code

PostCast: Generalizable Postprocessing for Precipitation Nowcasting via Unsupervised Blurriness Modeling

no code implementations8 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.

Denoising

FNP: Fourier Neural Processes for Arbitrary-Resolution Data Assimilation

no code implementations3 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.

Weather Forecasting

Feynman Diagrams as Computational Graphs

no code implementations28 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.

Towards an end-to-end artificial intelligence driven global weather forecasting system

1 code implementation18 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.

Weather Forecasting

Tree-Guided Rare Feature Selection and Logic Aggregation with Electronic Health Records Data

no code implementations18 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.

Dimensionality Reduction feature selection +1

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

Collaboration Equilibrium in Federated Learning

1 code implementation18 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.

Federated Learning

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.

feature selection regression

Survival Modeling of Suicide Risk with Rare and Uncertain Diagnoses

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.

Computational Efficiency regression +1

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.

feature selection regression

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 regression

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).

regression

Communication-Efficient Distributed SVD via Local Power Iterations

1 code implementation19 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.

regression

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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