Michael I. Jordan

TL;DR: The DAGGER algorithm, shorthand for Greedily Evolving Rejections on DAGs, provably controls the false discovery rate under independence, positive dependence or arbitrary dependence of the $p-values.

TL;DR: A negative answer to the question whether optimal local decision functions for the Bayesian formulation of sequential decentralized detection can be found within the class of stationary rules is provided by exploiting an asymptotic approximation to the optimal cost of stationary quantization rules, and the asymmetry of the Kullback-Leibler divergences.

TL;DR: A novel algorithm that solves semidefinite programs (SDPs) via repeated optimization over randomly selected two-dimensional subcones of the PSD cone is presented, which is simple, easily implemented, applicable to very general SDPs, scalable, and theoretically interesting.

TL;DR: This paper uses nested stick-breaking processes to allow for trees of unbounded width and depth, where data can live at any node and are infinitely exchangeable, and applies the method to hierarchical clustering of images and topic modeling of text data.

TL;DR: In this paper, it was shown that the Wasserstein projection pursuit (WPP) distance can be efficiently computed in practice using Riemannian optimization, yielding in relevant cases better behavior than its convex relaxation.