29 citations · 65 across the 21 of their papers we have counts for
21 papers
Learning Acceptance Regions for Many Classes with Anomaly Detection
Zhou Wang, Xingye Qiao
Set-valued classification, a new classification paradigm that aims to identify all the plausible classes that an observation belongs to, can be obtained by learning the acceptance…
Enhanced Nearest Neighbor Classification for Crowdsourcing
Jiexin Duan, Xingye Qiao, Guang Cheng
In machine learning, crowdsourcing is an economical way to label a large amount of data. However, the noise in the produced labels may deteriorate the accuracy of any classificatio…
Covariance-engaged Classification of Sets via Linear Programming
Zhao Ren, Sungkyu Jung, Xingye Qiao
Set classification aims to classify a set of observations as a whole, as opposed to classifying individual observations separately. To formally understand the unfamiliar concept of…
Near-optimal Individualized Treatment Recommendations
Haomiao Meng, Ying-Qi Zhao, Haoda Fu +1
Individualized treatment recommendation (ITR) is an important analytic framework for precision medicine. The goal is to assign proper treatments to patients based on their individu…
Doubly Robust Direct Learning for Estimating Conditional Average Treatment Effect
Haomiao Meng, Xingye Qiao
Inferring the heterogeneous treatment effect is a fundamental problem in the sciences and commercial applications. In this paper, we focus on estimating Conditional Average Treatme…
Maximum Entropy Diverse Exploration: Disentangling Maximum Entropy Reinforcement Learning
Andrew Cohen, Lei Yu, Xingye Qiao +1
Two hitherto disconnected threads of research, diverse exploration (DE) and maximum entropy RL have addressed a wide range of problems facing reinforcement learning algorithms via…
Rates of Convergence for Large-scale Nearest Neighbor Classification
Xingye Qiao, Jiexin Duan, Guang Cheng
Nearest neighbor is a popular class of classification methods with many desirable properties. For a large data set which cannot be loaded into the memory of a single machine due to…
Towards Run Time Estimation of the Gaussian Chemistry Code for SEAGrid Science Gateway
Angel Beltre, Shehtab Zaman, Kenneth Chiu +3
Accurate estimation of the run time of computational codes has a number of significant advantages for scientific computing. It is required information for optimal resource allocati…
Diverse Exploration via Conjugate Policies for Policy Gradient Methods
Andrew Cohen, Xingye Qiao, Lei Yu +2
We address the challenge of effective exploration while maintaining good performance in policy gradient methods. As a solution, we propose diverse exploration (DE) via conjugate po…
Distributed Nearest Neighbor Classification
Jiexin Duan, Xingye Qiao, Guang Cheng
Nearest neighbor is a popular nonparametric method for classification and regression with many appealing properties. In the big data era, the sheer volume and spatial/temporal disp…
Learning Confidence Sets using Support Vector Machines
Wenbo Wang, Xingye Qiao
The goal of confidence-set learning in the binary classification setting is to construct two sets, each with a specific probability guarantee to cover a class. An observation outsi…
Probabilistic Models for Daily Peak Loads at Distribution Feeders
Hossein Sangrody, Ning Zhou, Xingye Qiao
Load forecasting at distribution networks is more challenging than load forecasting at transmission networks because its load pattern is more stochastic and unpredictable. To plan…
On Reject and Refine Options in Multicategory Classification
Chong Zhang, Wenbo Wang, Xingye Qiao
In many real applications of statistical learning, a decision made from misclassification can be too costly to afford; in this case, a reject option, which defers the decision unti…
Significance Analysis of High-Dimensional, Low-Sample Size Partially Labeled Data
Qiyi Lu, Xingye Qiao
Classification and clustering are both important topics in statistical learning. A natural question herein is whether predefined classes are really different from one another, or w…
Sparse Fisher's Linear Discriminant Analysis for Partially Labeled Data
Qiyi Lu, Xingye Qiao
Classification is an important tool with many useful applications. Among the many classification methods, Fisher's Linear Discriminant Analysis (LDA) is a traditional model-based a…
Noncrossing Ordinal Classification
Xingye Qiao
Ordinal data are often seen in real applications. Regular multicategory classification methods are not designed for this data type and a more proper treatment is needed. We conside…
Stabilized Nearest Neighbor Classifier and Its Statistical Properties
Wei Sun, Xingye Qiao, Guang Cheng
The stability of statistical analysis is an important indicator for reproducibility, which is one main principle of scientific method. It entails that similar statistical conclusio…
Significance Analysis for Pairwise Variable Selection in Classification
Xingye Qiao, Yufeng Liu, J. S. Marron
The goal of this article is to select important variables that can distinguish one class of data from another. A marginal variable selection method ranks the marginal effects for c…
Regularization after retention in ultrahigh dimensional linear regression models
Haolei Weng, Yang Feng, Xingye Qiao
In ultrahigh dimensional setting, independence screening has been both theoretically and empirically proved a useful variable selection framework with low computation cost. In this…
Flexible High-dimensional Classification Machines and Their Asymptotic Properties
Xingye Qiao, Lingsong Zhang
Classification is an important topic in statistics and machine learning with great potential in many real applications. In this paper, we investigate two popular large margin class…
Distance-weighted Support Vector Machine
Xingye Qiao, Lingsong Zhang
A novel linear classification method that possesses the merits of both the Support Vector Machine (SVM) and the Distance-weighted Discrimination (DWD) is proposed in this article.…