We study the problem of PAC learning one-hidden-layer ReLU networks with $ k $ hidden
units on $\mathbb {R}^ d $ under Gaussian marginals in the presence of additive label …

We study the power of query access for the fundamental task of agnostic learning under the
Gaussian distribution. In the agnostic model, no assumptions are made on the labels of the …

Specifying all desirable properties of a language model is challenging, but certain
requirements seem essential. Given samples from an unknown language, the trained model …

We study the fundamental problem of transfer learning where a learning algorithm collects
data from some source distribution $ P $ but needs to perform well with respect to a different …

For many learning problems one may not have access to fine grained label information; eg,
an image can be labeled as husky, dog, or even animal depending on the expertise of the …

Missing data problems have many manifestations across many scientific fields. A
fundamental type of missing data problem arises when samples are\textit {truncated}, ie …

We study the basic statistical problem of testing whether normally distributed n-dimensional
data has been truncated, ie altered by only retaining points that lie in some unknown …

Machine Learning (ML) is increasingly used across many disciplines with impressive
reported results. However, recent studies suggest published performance of ML models are …

We study the estimation of distributional parameters when samples are shown only if they
fall in some unknown set. Kontonis, Tzamos, and Zampetakis (FOCS'19) gave an algorithm …

We consider the following basic, and very broad, statistical problem: Given a known high-
dimensional distribution D over ℝ n and a collection of data points in ℝ n, distinguish …