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…

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…

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…

Abstract: The Bayesian approach to inverse problems has become very popular in the last decade after seminal work by Andrew Stuart (2010) and collaborators. Particularly in non-linear applications with PDEs and when using Gaussian process priors, this can leverage powerful MCMC methodology to tackle difficult high-dimensional and non-convex inference problems. Little is known in terms…