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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Proceedings Article
Probabilistic inference in queueing networks
Charles Sutton,Michael I. Jordan +1 more
TL;DR: This paper presents a Gibbs sampler and stochastic EM algorithm for networks of M/M/1 FIFO queues, and localizes performance problems in distributed systems from incomplete system trace data.
Posted Content
Mechanism Design with Bandit Feedback.
TL;DR: A multi-round welfare-maximising mechanism design problem in instances where agents do not know their values, and defines three notions of regret for the welfare, the individual utilities of each agent and that of the mechanism to control the degree of truthfulness and individual rationality.
Book ChapterDOI
On the inference of ancestries in admixed populations
TL;DR: While retaining the HMM as a model, it is shown that a combination of an accurate fast initialization and a local hill-climb in likelihood results in significantly improved estimates of ancestry.
Journal ArticleDOI
Detection of single peptide with only one amino acid modification via electronic fingerprinting using reengineered durable channel of Phi29 DNA packaging motor.
Long Zhang,Miranda L. Gardner,Lakmal Jayasinghe,Michael I. Jordan,Julian Aldana,Nicolas Burns,Michael A. Freitas,Peixuan Guo +7 more
TL;DR: In this article, a label-free method for the detection of the single peptide with only one amino acid modification via electronic fingerprinting using reengineered durable channel of phi29 DNA packaging motor, which bears the deletion of 25-amino acids (AA) at the C-terminus or 17-AA at the internal loop of the channel.
Journal Article
Optimization with Momentum: Dynamical, Control-Theoretic, and Symplectic Perspectives
TL;DR: In this article, the convergence rate of momentum-based optimization algorithms is analyzed from a dynamical system point of view, and closed-form expressions are obtained that relate algorithm parameters to convergence rate.