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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Proceedings ArticleDOI
Bayesian haplo-type inference via the dirichlet process
TL;DR: A Bayesian approach to the problem of inferring haplotypes from genotypes of single nucleotide polymorphisms based on a nonparametric prior known as the Dirichlet process is presented, which incorporates a likelihood that captures statistical errors in the haplotype/genotype relationship.
Proceedings Article
Robust Optimization for Fairness with Noisy Protected Groups
Serena Wang,Wenshuo Guo,Harikrishna Narasimhan,Andrew Cotter,Maya R. Gupta,Michael I. Jordan +5 more
TL;DR: Two new approaches using robust optimization are introduced that, unlike the na{i}ve approach of only relying on noisy protected group labels, are guaranteed to satisfy fairness criteria on the true protected groups $G$ while minimizing a training objective.
Posted Content
Parallel Correlation Clustering on Big Graphs
Xinghao Pan,Dimitris S. Papailiopoulos,Samet Oymak,Benjamin Recht,Kannan Ramchandran,Michael I. Jordan +5 more
TL;DR: C4 and ClusterWild!, two algorithms for parallel correlation clustering that run in a polylogarithmic number of rounds and achieve nearly linear speedups, provably are presented.
Proceedings Article
The Big Data Bootstrap
TL;DR: The Bag of Little Bootstraps (BLB) as mentioned in this paper is a new procedure which incorporates features of both the bootstrap and subsampling to obtain a robust, computationally efficient means of assessing estimator quality.
Journal ArticleDOI
A Randomization Test for Controlling Population Stratification in Whole-Genome Association Studies
TL;DR: This work proposes a method for evaluating the significance of association scores in whole-genome cohorts with stratification, a randomization test akin to a standard permutation test that achieves higher power and significantly better control over false-positive rates than do existing methods.