Neural networks (NNs) are the method of choice for building learning algorithms. They are
now being investigated for other numerical tasks such as solving high-dimensional partial …

Given a metric space, the (k, z)-clustering problem consists of finding k centers such that the
sum of the of distances raised to the power z of every point to its closest center is minimized …

Automatic recognition of bedridden patients' physical activity has important applications in
the clinical process. Such recognition tasks are usually accomplished on visual data …

In this work, we study fairness in centroid clustering. In this problem, k cluster centers must
be placed given n points in a metric space, and the cost to each point is its distance to the …

This article studies the problem of optimally allocating treatments in the presence of spillover
effects, using information from a (quasi-) experiment. I introduce a method that maximizes …

The existence of adversarial examples points to a basic weakness of deep neural networks.
One of the most effective defenses against such examples, adversarial training, entails …

We prove a characterization of the structural conditions on matrices of sign-rank 3 and unit
disk graphs (UDGs) which permit constant-cost public-coin randomized communication …

Abstract The Vapnik–Chervonenkis dimension provides a notion of complexity for systems of
sets. If the VC dimension is small, then knowing this can drastically simplify fundamental …

We consider the problem of determining which classes of functions can be tested more
efficiently than they can be learned, in the distribution-free sample-based model that …

We initiate the study of fairness for ordinal regression. We adapt two fairness notions
previously considered in fair ranking and propose a strategy for training a predictor that is …