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.
Papers
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Journal ArticleDOI
Multiway Spectral Clustering: A Margin-based Perspective
Zhihua Zhang,Michael I. Jordan +1 more
TL;DR: In this paper, a margin-based perspective on multiway spectral clustering is presented, which illuminates both the relaxation and rounding aspects of clustering, providing a unified analysis of existing algorithms and guiding the design of new algorithms.
Proceedings Article
Unsupervised Kernel Dimension Reduction
TL;DR: Kernel-based measures of independence are used to derive low-dimensional representations that maximally capture information in covariates in order to predict responses and the resulting compact representation yields meaningful and appealing visualization and clustering of data.
Posted Content
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: SMaSH as mentioned in this paper is a benchmarking methodology for evaluating human genome variant calling algorithms, including single nucleotide polymorphism (SNP), indel, and structural variant calling.
Proceedings ArticleDOI
A statistical approach to decision tree modeling
TL;DR: A statistical approach to decision tree modeling is described, in which each decision in the tree is modeled parametrically as is the process by which an output is generated from an input and a sequence of decisions, yielding a likelihood measure of goodness of fit.
A Case For Adaptive Datacenters To Conserve Energy and Improve Reliability
Peter Bodik,Michael Armbrust,Kevin Robert Canini,Armando Fox,Michael I. Jordan,David A. Patterson +5 more
TL;DR: This paper identifies a new approach based on the synergy between virtual machines and statistical machine learning, and observes that constrained energy conservation can improve hardware reliability, and gives initial results on a cluster that reduces energy costs, reduces integrated circuit failures, and disk failures.