Learning in the presence of outliers is a fundamental problem in statistics. Until recently, all
known efficient unsupervised learning algorithms were very sensitive to outliers in high …

Recent works have shown that diffusion models can learn essentially any distribution
provided one can perform score estimation. Yet it remains poorly understood under what …

We study high-dimensional distribution learning in an agnostic setting where an adversary is
allowed to arbitrarily corrupt an ε-fraction of the samples. Such questions have a rich history …

In high dimensions, most machine learning methods are brittle to even a small fraction of
structured outliers. To address this, we introduce a new meta-algorithm that can take in a …

Outlier-robust estimation is a fundamental problem and has been extensively investigated
by statisticians and practitioners. The last few years have seen a convergence across …

We study the problem of private distribution learning with access to public data. In this setup,
which we refer to as* public-private learning*, the learner is given public and private …

In statistical learning and analysis from shared data, which is increasingly widely adopted in
platforms such as federated learning and meta-learning, there are two major concerns …

A first line of attack in exploratory data analysis is\emph {data visualization}, ie, generating a
2-dimensional representation of data that makes\emph {clusters} of similar points visually …

Many modern machine learning classifiers are shown to be vulnerable to adversarial
perturbations of the instances. Despite a massive amount of work focusing on making …

We study the prototypical problem of high-dimensional linear regression in a robust model
where an ε-fraction of the samples can be adversarially corrupted. We focus on the …