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 Article
Statistical Debugging of Sampled Programs
TL;DR: A novel strategy for automatically debugging programs given sampled data from thousands of actual user runs that has analogies with intuitive debugging heuristics, and is able to deal with various types of bugs that occur in real programs.
Journal ArticleDOI
Active site prediction using evolutionary and structural information
TL;DR: A new method is presented, Discern, which provides a significant improvement over the state-of-the-art through the use of statistical techniques to derive a model with a small set of features that are jointly predictive of enzyme active sites.
Journal ArticleDOI
A dual-constriction biological nanopore resolves homonucleotide sequences with high fidelity.
Sander E Van der Verren,Nani Van Gerven,Wim Jonckheere,Richard George Hambley,Pratik Raj Singh,John Joseph Kilgour,Michael I. Jordan,E. Jayne Wallace,Lakmal Jayasinghe,Han Remaut +9 more
TL;DR: DNA sequencing using a prototype CsgG–CsgF protein pore with two constrictions improved single-read accuracy by 25 to 70% in homopolymers up to 9 nucleotides long andEquipping a protein nanopore with a second constriction improves sequencing of homopolymer DNA stretches.
Proceedings Article
Large Margin Classifiers: Convex Loss, Low Noise, and Convergence Rates
TL;DR: It is shown that the statistical consequences of using a convex surrogate of the 0-1 loss function satisfy a pointwise form of Fisher consistency for classification and gives nontrivial bounds under the weakest possible condition on the loss function.
Posted Content
Efficient Stepwise Selection in Decomposable Models
TL;DR: A simple characterization for the edges that can be added to a decomposable model while retaining its decomposability and an efficient algorithm for enumerating all such edges for a given decomposables model in O(n2) time, where n is the number of variables in the model.