Michael I. Jordan

TL;DR: A provably fast \textit{deterministic} variant of the celebrated iterative Bregman projection (IBP) algorithm, named \textsc{FastIBP), is developed with a complexity bound of $\tilde{O}(mn^{7/3}\varepsilon^{-4/3})$, where $\varePSilon \in (0, 1)$ is the tolerance.

TL;DR: This work considers derivative-free algorithms for stochastic optimization problems that use only noisy function values rather than gradients, analyzing their finite-sample convergence rates and shows that if pairs of function values are available, algorithms that use gradient estimates based on random perturbations suffer a factor of at most √d in convergence rate over traditional Stochastic gradient methods.

TL;DR: In this article, a Bayesian nonparametric approach to speaker diarization is proposed, which builds on the hierarchical Dirichlet process hidden Markov model (HDP-HMM) of Teh et al.

TL;DR: New complexity results for several algorithms that approximately solve the regularized optimal transport (OT) problem between two discrete probability measures with at most $n$ atoms are presented and accelerated Sinkhorn and Greenkhorn algorithms that achieve the complexity bound of $\widetilde{\mathcal{O}}(n^{7/3}\varepsilon^{-1})$ are introduced.

TL;DR: A variational inference algorithm for efficient probabilistic inference in dense graphical models for the QMR-DT database is described, the accuracy of the algorithm is evaluated on a set of standard diagnostic cases and it is compared to stochastic sampling methods.