Pre-trained transformer models have demonstrated success across many nat...
In statistical learning theory, determining the sample complexity of
rea...
We study dynamic algorithms robust to adaptive input generated from sour...
The one-inclusion graph algorithm of Haussler, Littlestone, and Warmuth
...
We study fundamental problems in linear algebra, such as finding a maxim...
In the classical setting of self-selection, the goal is to learn k model...
We provide efficient estimation methods for first- and second-price auct...
Randomized Hadamard Transforms (RHTs) have emerged as a computationally
...
Recently (Elkin, Filtser, Neiman 2017) introduced the concept of a termi...
We consider the phenomenon of adversarial examples in ReLU networks with...
Daniely and Schacham recently showed that gradient descent finds adversa...
We study the problem of heavy-tailed mean estimation in settings where t...
We provide a static data structure for distance estimation which support...
We study the problem of high-dimensional robust linear regression where ...
Learning from data in the presence of outliers is a fundamental problem ...
We study efficient algorithms for linear regression and covariance estim...
We study the problem of identity testing of markov chains. In this setti...
We propose an estimator for the mean of a random vector in R^d
that can ...