Search Results for author: Yen-Chi Chen

Found 22 papers, 7 papers with code

Residual Scheduling: A New Reinforcement Learning Approach to Solving Job Shop Scheduling Problem

no code implementations27 Sep 2023 Kuo-Hao Ho, Ruei-Yu Jheng, Ji-Han Wu, Fan Chiang, Yen-Chi Chen, Yuan-Yu Wu, I-Chen Wu

Interestingly in our experiments, our approach even reaches zero gap for 49 among 50 JSP instances whose job numbers are more than 150 on 20 machines.

Job Shop Scheduling Scheduling

Skeleton Regression: A Graph-Based Approach to Estimation with Manifold Structure

no code implementations19 Mar 2023 Zeyu Wei, Yen-Chi Chen

We introduce a new regression framework designed to deal with large-scale, complex data that lies around a low-dimensional manifold.

regression

Mode and Ridge Estimation in Euclidean and Directional Product Spaces: A Mean Shift Approach

1 code implementation16 Oct 2021 Yikun Zhang, Yen-Chi Chen

The set of local modes and the ridge lines estimated from a dataset are important summary characteristics of the data-generating distribution.

Linear Convergence of the Subspace Constrained Mean Shift Algorithm: From Euclidean to Directional Data

2 code implementations29 Apr 2021 Yikun Zhang, Yen-Chi Chen

This paper studies the linear convergence of the subspace constrained mean shift (SCMS) algorithm, a well-known algorithm for identifying a density ridge defined by a kernel density estimator.

Skeleton Clustering: Dimension-Free Density-based Clustering

2 code implementations21 Apr 2021 Zeyu Wei, Yen-Chi Chen

We introduce a density-based clustering method called skeleton clustering that can detect clusters in multivariate and even high-dimensional data with irregular shapes.

Clustering

The EM Perspective of Directional Mean Shift Algorithm

1 code implementation25 Jan 2021 Yikun Zhang, Yen-Chi Chen

Under the (generalized) EM framework, we provide a new proof for the ascending property of density estimates and demonstrate the global convergence of directional mean shift sequences.

Kernel Smoothing, Mean Shift, and Their Learning Theory with Directional Data

1 code implementation23 Oct 2020 Yikun Zhang, Yen-Chi Chen

Directional data consist of observations distributed on a (hyper)sphere, and appear in many applied fields, such as astronomy, ecology, and environmental science.

Astronomy Clustering +1

Nonparametric Pattern-Mixture Models for Inference with Missing Data

no code implementations24 Apr 2019 Yen-Chi Chen, Mauricio Sadinle

Pattern-mixture models provide a transparent approach for handling missing data, where the full-data distribution is factorized in a way that explicitly shows the parts that can be estimated from observed data alone, and the parts that require identifying restrictions.

Methodology Statistics Theory Statistics Theory

Statistical Inference with Local Optima

no code implementations12 Jul 2018 Yen-Chi Chen

We also investigate the CIs by inverting the likelihood ratio test, the score test, and the Wald test, and we show that the resulting CIs may be very different.

Test

Functional Summaries of Persistence Diagrams

1 code implementation4 Apr 2018 Eric Berry, Yen-Chi Chen, Jessi Cisewski-Kehe, Brittany Terese Fasy

First, we review the various functional summaries in the literature and propose a unified framework for the functional summaries.

Methodology

On the use of bootstrap with variational inference: Theory, interpretation, and a two-sample test example

no code implementations29 Nov 2017 Yen-Chi Chen, Y. Samuel Wang, Elena A. Erosheva

Variational inference is a general approach for approximating complex density functions, such as those arising in latent variable models, popular in machine learning.

Bayesian Inference Test +1

A Note on Community Trees in Networks

no code implementations11 Oct 2017 Ruqian Chen, Yen-Chi Chen, Wei Guo, Ashis G. Banerjee

We introduce the concept of community trees that summarizes topological structures within a network.

Statistical Inference Using Mean Shift Denoising

no code implementations13 Oct 2016 Yunhua Xiang, Yen-Chi Chen

In this paper, we study how the mean shift algorithm can be used to denoise a dataset.

Anomaly Detection Clustering +1

Statistical Inference for Cluster Trees

no code implementations NeurIPS 2016 Jisu Kim, Yen-Chi Chen, Sivaraman Balakrishnan, Alessandro Rinaldo, Larry Wasserman

A cluster tree provides a highly-interpretable summary of a density function by representing the hierarchy of its high-density clusters.

Statistical Analysis of Persistence Intensity Functions

no code implementations8 Oct 2015 Yen-Chi Chen, Daren Wang, Alessandro Rinaldo, Larry Wasserman

Persistence diagrams are two-dimensional plots that summarize the topological features of functions and are an important part of topological data analysis.

Clustering Topological Data Analysis

Statistical Inference using the Morse-Smale Complex

1 code implementation29 Jun 2015 Yen-Chi Chen, Christopher R. Genovese, Larry Wasserman

The Morse-Smale complex of a function $f$ decomposes the sample space into cells where $f$ is increasing or decreasing.

Clustering Density Estimation +2

Optimal Ridge Detection using Coverage Risk

no code implementations NeurIPS 2015 Yen-Chi Chen, Christopher R. Genovese, Shirley Ho, Larry Wasserman

We introduce the concept of coverage risk as an error measure for density ridge estimation.

Nonparametric modal regression

no code implementations4 Dec 2014 Yen-Chi Chen, Christopher R. Genovese, Ryan J. Tibshirani, Larry Wasserman

Modal regression estimates the local modes of the distribution of $Y$ given $X=x$, instead of the mean, as in the usual regression sense, and can hence reveal important structure missed by usual regression methods.

regression

A Comprehensive Approach to Mode Clustering

no code implementations6 Jun 2014 Yen-Chi Chen, Christopher R. Genovese, Larry Wasserman

Mode clustering is a nonparametric method for clustering that defines clusters using the basins of attraction of a density estimator's modes.

Clustering Denoising

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