Common statistical measures of uncertainty such as $ p $-values and confidence intervals
quantify the uncertainty due to sampling, that is, the uncertainty due to not observing the full …

I describe a game in which social ties between members of a research community may
discourage prospective replicators from debunking papers that misreport results. Here …

We introduce a fine-grained framework for uncertainty quantification of predictive models
under distributional shifts. This framework distinguishes the shift in covariate distributions …

Distributionally robust policy learning aims to find a policy that performs well under the worst-
case distributional shift, and yet most existing methods for robust policy learning consider …

Many existing approaches to generalizing statistical inference amidst distribution shift
operate under the covariate shift assumption, which posits that the conditional distribution of …

The rapid development of new data science technologies in recent decades has
revolutionized the ways in which people collect, store, transfer, and extract information from …