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
Supervised hierarchical Pitman-Yor process for natural scene segmentation
TL;DR: This paper adds label information into the previously unsupervised model by adding constraints on the parameter space during the variational learning phase and evaluates the effectiveness of the formulation on the La-belMe natural scene dataset.
Proceedings Article
Feature Selection Methods for Improving Protein Structure Prediction with Rosetta
TL;DR: This paper presents a resampling technique for structure prediction of small alpha/beta proteins using Rosetta that uses feature selection methods—both L1-regularized linear regression and decision trees—to identify structural features that give rise to low energy.
Posted ContentDOI
Deep Generative Models for Detecting Differential Expression in Single Cells
Pierre Boyeau,Pierre Boyeau,Romain Lopez,Jeffrey Regier,Adam Gayoso,Michael I. Jordan,Nir Yosef +6 more
TL;DR: This work shows that deep generative models, which combined Bayesian statistics and deep neural networks, better estimate the log-fold-change in gene expression levels between subpopulations of cells and introduces a technique for improving the posterior approximation, which improves differential expression performance.
Posted Content
An Analysis of the Convergence of Graph Laplacians
TL;DR: In this paper, the authors generalize the analysis of graph Laplacians to include previously unstudied graphs including kNN graphs and introduce a kernel-free framework to analyze graph constructions with shrinking neighborhoods.
Proceedings Article
Coherence Functions for Multicategory Margin-based Classification Methods
TL;DR: A new majorization loss function is proposed that is closely related to the multinomial log-likelihood function and its limit at zero temperature corresponds to a multicategory hinge loss function.