Learning models with uniform performance via distributionally robust optimization Page 1 The
Annals of Statistics 2021, Vol. 49, No. 3, 1378–1406 https://doi.org/10.1214/20-AOS2004 © …

This paper deals with the problem of quantifying the impact of model misspecification when
computing general expected values of interest. The methodology that we propose is …

We propose a simplified proof process for PAC-Bayesian generalization bounds, that allows
to divide the proof in four successive inequalities, easing the" customization" of PAC …

Input uncertainty refers to errors caused by a lack of complete knowledge about the
probability distributions used to generate input variates in stochastic simulation. The …

We develop a rigorous and general framework for constructing information-theoretic
divergences that subsume both f-divergences and integral probability metrics (IPMs), such …

This paper provides upper and lower bounds on the optimal guessing moments of a random
variable taking values on a finite set when side information may be available. These …

We introduce a gain function viewpoint of information leakage by proposing maximal-
leakage, a rich class of operationally meaningful leakage measures that subsumes recently …

We propose a framework to analyze and quantify the bias in adaptive data analysis. It
generalizes that proposed by Russo and Zou'15, applying to measurements whose moment …

We derive a new variational formula for the Rényi family of divergences, R_α(Q‖P),
between probability measures Q and P. Our result generalizes the classical Donsker …

We consider sensitivity of a generic stochastic optimization problem to model uncertainty.
We take a non-parametric approach and capture model uncertainty using Wasserstein balls …