With the constantly growing number of reviews and other sentiment-bearing texts on the
Web, the demand for automatic sentiment analysis algorithms continues to expand. Aspect …

The concepts of risk aversion, chance-constrained optimization, and robust optimization
have developed significantly over the last decade. The statistical learning community has …

We consider nonconvex-concave minimax problems, $\min_ {\mathbf {x}}\max_ {\mathbf
{y}\in\mathcal {Y}} f (\mathbf {x},\mathbf {y}) $, where $ f $ is nonconvex in $\mathbf {x} $ but …

Deep neural networks and machine-learning algorithms are pervasively used in several
applications, ranging from computer vision to computer security. In most of these …

We propose a method to learn deep ReLU-based classifiers that are provably robust against
norm-bounded adversarial perturbations on the training data. For previously unseen …

Abstract Machine learning models are often susceptible to adversarial perturbations of their
inputs. Even small perturbations can cause state-of-the-art classifiers with high" standard" …

Neural networks are vulnerable to adversarial examples and researchers have proposed
many heuristic attack and defense mechanisms. We address this problem through the …

Defenses against adversarial examples, such as adversarial training, are typically tailored to
a single perturbation type (eg, small $\ell_\infty $-noise). For other perturbations, these …

Current methods for training robust networks lead to a drop in test accuracy, which has led
prior works to posit that a robustness-accuracy tradeoff may be inevitable in deep learning …

This paper resolves a longstanding open question pertaining to the design of near-optimal
first-order algorithms for smooth and strongly-convex-strongly-concave minimax problems …