The field of property testing originated in work on program checking, and has evolved into
an established and very active research area. In this work, we survey the developments of …

Property testing is concerned with the design of super-fast algorithms for the structural
analysis of large quantities of data. The aim is to unveil global features of the data, such as …

A critical question concerning generative networks applied to event generation in particle
physics is if the generated events add statistical precision beyond the training sample. We …

We study the problem of learning under arbitrary distribution shift, where the learner is
trained on a labeled set from one distribution but evaluated on a different, potentially …

In this work, we study how to efficiently obtain perfect samples from a discrete distribution
$\mathcal {D} $ given access only to pairwise comparisons of elements of its support …

Given data drawn from an unknown distribution, D, to what extent is it possible to “amplify”
this dataset and faithfully output an even larger set of samples that appear to have been …

The field of property testing originated in work on program checking, and has evolved into
an established and very active research area. In this work, we survey the developments of …

This thesis explores questions surrounding the foundations of intelligence, both artificial and
natural. The first part focuses on the algorithmic and statistical underpinnings of modern …

In many practical applications, heuristic or approximation algorithms are used to efficiently
solve the task at hand. However their solutions frequently do not satisfy natural monotonicity …

A wide range of learning tasks require human input in labeling massive data. The collected
data though are usually low quality and contain inaccuracies and errors. As a result, modern …