15 citations · 35 across the 21 of their papers we have counts for
21 papers
Structure-Adaptive E-Value Filter for Detecting Regional Signals in Brain Imaging
Jingcheng He, Hangjin Jiang, Wenguang Sun +1
Structural MRI provides a noninvasive view of the neuroanatomical differences associated with cognitive impairment and dementia. Using data from the Alzheimer's Disease Neuroimagin…
COINS: Any-Stage-Valid and Utility-Oriented Sequential Conformal Prediction
Wangcheng Li, Nan Qiao, Xu Guo +1
In clinical diagnosis, sequential screening, and cascaded prediction systems, information is often acquired in stages, with intermediate predictions guiding whether to act on the a…
Conformalized Large-Scale Selective Inference with Informative and Trustworthy Prediction Sets
Wangcheng Li, Guanlan Zhao, Xu Guo +1
In large-scale prediction problems, exhaustively following up on all test units is often impractical and inefficient, motivating a selective reporting strategy that fulfills the du…
Structure-Adaptive Conformal Inference for Large-Scale Out-of-Distribution Testing
Rongyi Sun, Wenguang Sun, Zinan Zhao
This paper addresses structured out-of-distribution (OOD) testing in high-stakes machine learning applications. Traditional conformal methods rely on joint exchangeability, making…
Empirical Bayes Estimation with Side Information: A Nonparametric Integrative Tweedie Approach
Jiajun Luo, Trambak Banerjee, Gourab Mukherjee +1
We investigate the problem of compound estimation of normal means while accounting for the presence of side information. Leveraging the empirical Bayes framework, we develop a nonp…
Ranking and Selection in Large-Scale Inference of Heteroscedastic Units
Bowen Gang, Luella Fu, Gareth James +1
The allocation of limited resources to a large number of potential candidates presents a pervasive challenge. In the context of ranking and selecting top candidates from heterosced…
A Locally Adaptive Shrinkage Approach to False Selection Rate Control in High-Dimensional Classification
Bowen Gang, Yuantao Shi, Wenguang Sun
The uncertainty quantification and error control of classifiers are crucial in many high-consequence decision-making scenarios. We propose a selective classification framework that…
Integrative conformal p-values for powerful out-of-distribution testing with labeled outliers
Ziyi Liang, Matteo Sesia, Wenguang Sun
This paper develops novel conformal methods to test whether a new observation was sampled from the same distribution as a reference set. Blending inductive and transductive conform…
A Locally Adaptive Algorithm for Multiple Testing with Network Structure
Ziyi Liang, T. Tony Cai, Wenguang Sun +1
Incorporating auxiliary information alongside primary data can significantly enhance the accuracy of simultaneous inference. However, existing multiple testing methods face challen…
An Empirical Bayes Approach to Controlling the False Discovery Exceedance
Pallavi Basu, Luella Fu, Alessio Saretto +1
In large-scale multiple hypothesis testing problems, the false discovery exceedance (FDX) provides a desirable alternative to the widely used false discovery rate (FDR) when the fa…
A Burden Shared is a Burden Halved: A Fairness-Adjusted Approach to Classification
Bradley Rava, Wenguang Sun, Gareth M. James +1
We investigate the fairness issue in classification, where automated decisions are made for individuals from different protected groups. In high-consequence scenarios, decision err…
ZAP: -value Adaptive Procedures for False Discovery Rate Control with Side Information
Dennis Leung, Wenguang Sun
Adaptive multiple testing with covariates is an important research direction that has gained major attention in recent years. It has been widely recognized that leveraging side inf…
Large-Scale Shrinkage Estimation under Markovian Dependence
Bowen Gang, Gourab Mukherjee, Wenguang Sun
We consider the problem of simultaneous estimation of a sequence of dependent parameters that are generated from a hidden Markov model. Based on observing a noise contaminated vect…
Structure-Adaptive Sequential Testing for Online False Discovery Rate Control
Bowen Gang, Wenguang Sun, Weinan Wang
Consider the online testing of a stream of hypotheses where a real--time decision must be made before the next data point arrives. The error rate is required to be controlled at {a…
Nonparametric Empirical Bayes Estimation on Heterogeneous Data
Trambak Banerjee, Luella J. Fu, Gareth M. James +2
The simultaneous estimation of many parameters based on data collected from corresponding studies is a key research problem that has received renewed attention in the high-dimensio…
False Discovery Rate Control Under General Dependence By Symmetrized Data Aggregation
Lilun Du, Xu Guo, Wenguang Sun +1
We develop a new class of distribution--free multiple testing rules for false discovery rate (FDR) control under general dependence. A key element in our proposal is a symmetrized…
Heterocedasticity-Adjusted Ranking and Thresholding for Large-Scale Multiple Testing
Luella Fu, Bowen Gang, Gareth M. James +1
Standardization has been a widely adopted practice in multiple testing, for it takes into account the variability in sampling and makes the test statistics comparable across differ…
Adaptive Sparse Estimation with Side Information
Trambak Banerjee, Gourab Mukherjee, Wenguang Sun
The article considers the problem of estimating a high-dimensional sparse parameter in the presence of side information that encodes the sparsity structure. We develop a general fr…
Optimal design for high-throughput screening via false discovery rate control
Tao Feng, Pallavi Basu, Wenguang Sun +2
High-throughput screening (HTS) is a large-scale hierarchical process in which a large number of chemicals are tested in multiple stages. Conventional statistical analyses of HTS s…
Sparse Recovery With Multiple Data Streams: A Sequential Adaptive Testing Approach
Weinan Wang, Bowen Gang, Wenguang Sun
Multistage design has been used in a wide range of scientific fields. By allocating sensing resources adaptively, one can effectively eliminate null locations and localize signals…
Weighted False Discovery Rate Control in Large-Scale Multiple Testing
Pallavi Basu, T. Tony Cai, Kiranmoy Das +1
The use of weights provides an effective strategy to incorporate prior domain knowledge in large-scale inference. This paper studies weighted multiple testing in a decision-theoret…