4 papers
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…
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…