We ask the question: how can Web sites and data aggregators continually release updated
statistics, and meanwhile preserve each individual user's privacy? Suppose we are given a …

We develop an approach for estimating models described via conditional moment
restrictions, with a prototypical application being non-parametric instrumental variable …

Abstract Generalized Linear Models (GLMs) and Single Index Models (SIMs) provide
powerful generalizations of linear regression, where the target variable is assumed to be a …

Estimating Wasserstein distances between two high-dimensional densities suffers from the
curse of dimensionality: one needs an exponential (wrt dimension) number of samples to …

A semiparametric, nonlinear regression model in the presence of latent variables is
introduced. These latent variables can correspond to unmodeled phenomena or …

Loss functions are a cornerstone of machine learning and the starting point of most
algorithms. Statistics and Bayesian decision theory have contributed, via properness, to elicit …

Biogeochemical models, capturing the major feedbacks of the pelagic ecosystem of the
world ocean, are today often embedded into Earth system models which are increasingly …

This dissertation addresses the task of learning to rank, both in the supervised and
unsupervised settings, by exploiting the interplay of convex functions, monotonic mappings …

In this paper, we introduce an optimality condition that, roughly formulated, says that an
approximate gradient projection tends to zero. For this reason, we call it approximate …

The usual estimators of the regression under isotonicity assumptions are too sensitive at the
tails. In order to avoid this problem, some new strategies for fixed designs are analyzed. The …