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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Detecting Zero-Inflated Genes in Single-Cell Transcriptomics Data
TL;DR: The AutoZI model, which, for each gene, places a spike-and-slab prior on a mixture assignment between a negative binomial (NB) component and a zero-inflated negative Binomial (ZINB) component, allows both biological and technical interpretations of zero-Inflation.
Posted Content
Covariance estimation with nonnegative partial correlations
TL;DR: This work establishes that the Gaussian maximum likelihood estimator is both high-dimensionally consistent and minimax optimal in the symmetrized Stein loss and proves a negative result which shows that the sign-constraints can introduce substantial bias for estimating the top eigenvalue of the covariance matrix.
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS) 2010
Charles Sutton,Michael I. Jordan +1 more
Posted Content
LS-Tree: Model Interpretation When the Data Are Linguistic
Jianbo Chen,Michael I. Jordan +1 more
TL;DR: This work proposes to assign least-squares-based importance scores to each word of an instance by exploiting syntactic constituency structure and establishes an axiomatic characterization of these importance scores by relating them to the Banzhaf value in coalitional game theory.
Proceedings Article
Representation Matters: Assessing the Importance of Subgroup Allocations in Training Data
TL;DR: By casting data collection as part of the learning process, it is demonstrated that diverse representation in training data is key not only to increasing subgroup performances, but also to achieving population-level objectives.