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
More filters
Journal ArticleDOI
Deep generative modeling for single-cell transcriptomics.
TL;DR: Single-cell variational inference (scVI) is a ready-to-use generative deep learning tool for large-scale single-cell RNA-seq data that enables raw data processing and a wide range of rapid and accurate downstream analyses.
Posted Content
Conditional Adversarial Domain Adaptation
TL;DR: Conditional adversarial domain adaptation is presented, a principled framework that conditions the adversarial adaptation models on discriminative information conveyed in the classifier predictions to guarantee the transferability.
Journal ArticleDOI
Convergence of Stochastic Iterative Dynamic Programming Algorithms
TL;DR: A rigorous proof of convergence of DP-based learning algorithms is provided by relating them to the powerful techniques of stochastic approximation theory via a new convergence theorem, which establishes a general class of convergent algorithms to which both TD() and Q-learning belong.
Proceedings ArticleDOI
Kalman filtering with intermittent observations
Bruno Sinopoli,Luca Schenato,Massimo Franceschetti,Kameshwar Poolla,Michael I. Jordan,S. Shankar Sastry +5 more
TL;DR: This work studies the statistical convergence properties of the estimation error covariance, showing the existence of a critical value for the arrival rate of the observations, beyond which a transition to an unbounded error occurs.