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
Variational methods for the Dirichlet process
David M. Blei,Michael I. Jordan +1 more
TL;DR: A mean-field variational approach to approximate inference for the Dirichlet process, where the approximate posterior is based on the truncated stick-breaking construction (Ishwaran & James, 2001).
Proceedings Article
Efficient Ranking from Pairwise Comparisons
TL;DR: If an average of O(n log(n) binary comparisons are measured, then one algorithm recovers the true ranking in a uniform sense, while the other predicts the ranking more accurately near the top than the bottom.
Journal ArticleDOI
Constrained and Unconstrained Movements Involve Different Control Strategies
TL;DR: The data support the hypothesis that unconstrained motions are, unlike compliant motions, not programmed to follow a straight-line path in the task space, and suggest that compliant and unconStrained movements involve different control strategies.
Journal ArticleDOI
Toward a protein profile of Escherichia coli: comparison to its transcription profile.
Rebecca W. Corbin,Oleg Paliy,Feng Yang,Jeffrey Shabanowitz,Mark D. Platt,Charles E. Lyons,Karen E. Root,Jon McAuliffe,Michael I. Jordan,Sydney Kustu,Eric Soupene,Donald F. Hunt +11 more
TL;DR: GeneChip data confirmed the high reliability of the protein list, which contains about one-fourth of the proteins of E. coli, and detection of even those membrane proteins and proteins of undefined function that are encoded by the same operons (transcriptional units) encoding proteins on the list remained low.
Proceedings Article
Bridging Theory and Algorithm for Domain Adaptation.
TL;DR: In this article, the authors address the problem of unsupervised domain adaption from theoretical and algorithmic perspectives by extending previous theories to multiclass classification in domain adaptation, where classifiers based on scoring functions and margin loss are standard choices in algorithm design.