Privacy-preserving statistical data analysis addresses the general question of protecting
privacy when publicly releasing information about a sensitive dataset. A privacy attack takes …

Artificial Intelligence (AI) has attracted a great deal of attention in recent years. However,
alongside all its advancements, problems have also emerged, such as privacy violations …

Differential privacy is a theoretical framework for ensuring the privacy of individual-level data
when performing statistical analysis of privacy-sensitive datasets. This tutorial provides an …

We provide an information-theoretic framework for studying the generalization properties of
machine learning algorithms. Our framework ties together existing approaches, including …

A great deal of effort has been devoted to reducing the risk of spurious scientific discoveries,
from the use of sophisticated validation techniques, to deep statistical methods for …

Adaptivity is an important feature of data analysis-the choice of questions to ask about a
dataset often depends on previous interactions with the same dataset. However, statistical …

Overfitting is the bane of data analysts, even when data are plentiful. Formal approaches to
understanding this problem focus on statistical inference and generalization of individual …

Modern big data settings often involve messy, high-dimensional data, where it is not clear a
priori what are the right questions to ask. To extract the most insights from a dataset, the …

Modern data is messy and high-dimensional, and it is often not clear a priori what are the
right questions to ask. Instead, the analyst typically needs to use the data to search for …

Applying computational statistics or machine learning methods to data is a key component of
many scientific studies, in any field, but alone might not be sufficient to generate robust and …