4 citations · 6 across the 9 of their papers we have counts for
11 papers
Neyman-Pearson Classification with Indecisions: Simultaneous Control of Selective Type I and Type II Errors
Bradley Rava, Bowen Gang
Binary classification often requires controlling both type I and type II error rates at prespecified levels. When the class distributions overlap substantially, the two error limit…
Operator learning for the 2D incompressible Navier-Stokes equations: a conformal prediction approach in the data-scarce regime
Weinan Wang, Bowen Gang, Hao Deng
In this paper, we propose a perturbation-based conformal prediction framework for uncertainty quantification in operator learning, with a focus on the 2D Navier--Stokes equations.…
Safe, Always-Valid Alpha-Investing Rules For Doubly Sequential Online Inference
Zeyu Yao, Bowen Gang, Wenguang Sun
Dynamic decision-making in rapidly evolving research domains, including marketing, finance, and pharmaceutical development, presents a significant challenge. Researchers frequently…
Harnessing The Collective Wisdom: Fusion Learning Using Decision Sequences From Diverse Sources
Trambak Banerjee, Bowen Gang, Jianliang He
We introduce an Integrative Ranking and Thresholding (IRT) framework for fusing evidence from multiple testing procedures. The key innovation is a method that transforms binary tes…
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…
Large-Scale Multiple Testing of Composite Null Hypotheses Under Heteroskedasticity
Bowen Gang, Trambak Banerjee
Heteroskedasticity poses several methodological challenges in designing valid and powerful procedures for simultaneous testing of composite null hypotheses. In particular, the conv…
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…
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…
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…
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…