Search Results for author:
Found 22 papers, 7 papers with code
Residual Scheduling: A New Reinforcement Learning Approach to Solving Job Shop Scheduling Problem
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.
Skeleton Regression: A Graph-Based Approach to Estimation with Manifold Structure
We introduce a new regression framework designed to deal with large-scale, complex data that lies around a low-dimensional manifold.
Mode and Ridge Estimation in Euclidean and Directional Product Spaces: A Mean Shift Approach
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
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
We introduce a density-based clustering method called skeleton clustering that can detect clusters in multivariate and even high-dimensional data with irregular shapes.
The EM Perspective of Directional Mean Shift Algorithm
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
Directional data consist of observations distributed on a (hyper)sphere, and appear in many applied fields, such as astronomy, ecology, and environmental science.
Polygames: Improved Zero Learning
Since DeepMind's AlphaZero, Zero learning quickly became the state-of-the-art method for many board games.
Algorithms and Statistical Models for Scientific Discovery in the Petabyte Era
The field of astronomy has arrived at a turning point in terms of size and complexity of both datasets and scientific collaboration.
Nonparametric Pattern-Mixture Models for Inference with Missing Data
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
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.
Functional Summaries of Persistence Diagrams
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
Variational inference is a general approach for approximating complex density functions, such as those arising in latent variable models, popular in machine learning.
A Note on Community Trees in Networks
We introduce the concept of community trees that summarizes topological structures within a network.
Statistical Inference Using Mean Shift Denoising
In this paper, we study how the mean shift algorithm can be used to denoise a dataset.
Statistical Inference for Cluster Trees
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
Persistence diagrams are two-dimensional plots that summarize the topological features of functions and are an important part of topological data analysis.
Statistical Inference using the Morse-Smale Complex
The Morse-Smale complex of a function $f$ decomposes the sample space into cells where $f$ is increasing or decreasing.
Optimal Ridge Detection using Coverage Risk
We introduce the concept of coverage risk as an error measure for density ridge estimation.
Nonparametric modal regression
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.
A Comprehensive Approach to Mode Clustering
Mode clustering is a nonparametric method for clustering that defines clusters using the basins of attraction of a density estimator's modes.
