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 ArticleDOI
Multiple kernel learning, conic duality, and the SMO algorithm
TL;DR: Experimental results are presented that show that the proposed novel dual formulation of the QCQP as a second-order cone programming problem is significantly more efficient than the general-purpose interior point methods available in current optimization toolboxes.
Proceedings Article
Deep transfer learning with joint adaptation networks
TL;DR: JAN as mentioned in this paper aligns the joint distributions of multiple domain-specific layers across domains based on a joint maximum mean discrepancy (JMMD) criterion to make the distributions of the source and target domains more distinguishable.
Posted Content
Loopy Belief Propagation for Approximate Inference: An Empirical Study
TL;DR: In this article, the authors compare the performance of loopy belief propagation with the exact ones in four real world networks, including two real-world networks: ALARM and QMR, and find that the loopy beliefs often converge and when they do, they give a good approximation to the correct marginals.
Proceedings Article
Loopy belief propagation for approximate inference: an empirical study
TL;DR: This paper compares the marginals computed using loopy propagation to the exact ones in four Bayesian network architectures, including two real-world networks: ALARM and QMR, and finds that the loopy beliefs often converge and when they do, they give a good approximation to the correct marginals.
Journal ArticleDOI
Variational Inference for Dirichlet Process Mixtures
David M. Blei,Michael I. Jordan +1 more
TL;DR: A variational inference algorithm forDP mixtures is presented and experiments that compare the algorithm to Gibbs sampling algorithms for DP mixtures of Gaussians and present an application to a large-scale image analysis problem are presented.