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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Probabilistic graphical models and algorithms for genomic analysis
TL;DR: This thesis discusses two probabilistic modeling problems arising in metazoan genomic analysis: identifying motifs and cis-regulatory modules (CRMs) from transcriptional regulatory sequences, and inferring haplotypes from genotypes of single nucleotide polymorphisms.
Statistical software debugging
TL;DR: This work designs a utility function whose components may be adjusted based on the suspected level of determinism of the bug and presents an iterative predicate scoring algorithm, which proves to work well on two real world programs.
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A Bayesian Perspective of Convolutional Neural Networks through a Deconvolutional Generative Model
TL;DR: The Deconvolutional Generative Model (DGM), a new probabilistic generative model whose inference calculations correspond to those in a given CNN architecture, and a new loss termed as the Max-Min cross entropy which outperforms the traditional cross-entropy loss for object classification.
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Post-Selection Inference via Algorithmic Stability
Tijana Zrnic,Michael I. Jordan +1 more
TL;DR: This work revisit the PoSI problem through the lens of algorithmic stability, and shows that stability parameters of a selection method alone suffice to provide non-trivial corrections to classical z-test and t-test intervals.