Abstract: In 1993, Stephen A. Vavasis proved that in any finite dimension, there exists a faster method than gradient descent to find stationary points of smooth non-convex functions. In dimension 2 he proved that 1/eps gradient queries are enough, and that 1/sqrt(eps) queries are necessary. We close this gap by providing an algorithm based on a new local-to-global phenomenon for smooth non-convex functions. Some higher dimensional results will also be discussed. I will also present an extension of the 1/sqrt(eps)…

Abstract: Stein’s method is a key method for assessing distributional distance, mainly for one-dimensional distributions. In this talk we provide a general approach to Stein’s method for multivariate continuous distributions. Among the applications we consider is the Wasserstein distance between two continuous probability distributions under the assumption of existence of a Poincare constant. This is joint work with Guillaume Mijoule (INRIA Paris) and Yvik Swan (Liege). - Bio: Gesine Reinert is a Research Professor of the Department of Statistics and…

Abstract:  Massive data collection holds the promise of a better understanding of complex phenomena and ultimately, of better decisions. An exciting opportunity in this regard stems from the growing availability of perturbation / intervention data (drugs, knockouts, overexpression, etc.) in biology. In order to obtain mechanistic insights from such data, a major challenge is the development of a framework that integrates observational and interventional data and allows predicting the effect of yet unseen interventions or transporting the effect of interventions…

Abstract: The contextual bandit is a sequential decision making problem in which a learner repeatedly selects an action (e.g., a news article to display) in response to a context (e.g., a user’s profile) and receives a reward, but only for the action they selected. Beyond the classic explore-exploit tradeoff, a fundamental challenge in contextual bandits is to develop algorithms that can leverage flexible function approximation to model similarity between contexts, yet have computational requirements comparable to classical supervised learning tasks…