M
Michael I. Jordan
Researcher at University of California, Berkeley
Publications - 1110
Citations - 241763
Michael I. Jordan is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Computer science & Inference. The author has an hindex of 176, co-authored 1016 publications receiving 216204 citations. Previous affiliations of Michael I. Jordan include Stanford University & Princeton University.
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On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms
TL;DR: In this article, a greedy variant of the Sinkhorn algorithm, known as the \emph{Greenkhorn algorithm}, was improved to O(n^2\varepsilon^{-2}) by using a primal-dual formulation and an upper bound for the dual solution.
Journal ArticleDOI
SMaSH: a benchmarking toolkit for human genome variant calling
Ameet Talwalkar,Jesse Liptrap,Julie Newcomb,Christopher Hartl,Jonathan Terhorst,Kristal Curtis,Ma'ayan Bresler,Yun S. Song,Michael I. Jordan,David A. Patterson +9 more
TL;DR: The proposed SMaSH, a benchmarking methodology for evaluating germline variant calling algorithms, generates synthetic datasets, organizes and interpret a wide range of existing benchmarking data for real genomes and proposes a set of accuracy and computational performance metrics for evaluating variant calling methods on these benchmarked data.
Proceedings ArticleDOI
Real-Time Machine Learning: The Missing Pieces
Robert Nishihara,Philipp Moritz,Stephanie Wang,Alexey Tumanov,William Paul,Johann Schleier-Smith,Richard Liaw,Mehrdad Niknami,Michael I. Jordan,Ion Stoica +9 more
TL;DR: It is asserted that a new distributed execution framework is needed for such ML applications and a candidate approach with a proof-of-concept architecture that achieves a 63x performance improvement over a state- of-the-art execution framework for a representative application is proposed.
Proceedings ArticleDOI
Nonparametric estimation of the likelihood ratio and divergence functionals
TL;DR: This work develops and analyzes a nonparametric method for estimating the class of f-divergence functionals, and the density ratio of two probability distributions, and obtains an M-estimator for divergences, based on a convex and differentiable optimization problem that can be solved efficiently.
Proceedings Article
Variational Consensus Monte Carlo
TL;DR: The variational consensus Monte Carlo (VCMC) as mentioned in this paper is a variational Bayes algorithm that optimizes over aggregation functions to obtain samples from a distribution that better approximates the target.