We show how to combine techniques from formal methods and learning for online computation of a strategy that aims at optimizing the expected long-term reward in large systems modelled as Markov decision processes.

We formulate a general cross validation framework for signal denoising. The general framework is then applied to nonparametric regression methods such as Trend Filtering and Dyadic CART.

Hyperbolicity cones are convex semialgebraic sets generalizing both polyhedral and spectrahedral cones, the latter forming the basic geometric sets from linear and semidefinite programming.

We present a very simple and efficient sampling-based algorithm for estimating the union of sets in the streaming setting. Suppose we have a collection of sets S_1, . . .

The Gaussian correlation inequality (GCI), proven by Royen in 2014, states that any two centrally symmetric convex sets (say K and L) in the Gaussian space are positively correlated.