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
Posted Content
Is Q-learning Provably Efficient?
TL;DR: Q-learning with UCB exploration achieves regret in an episodic MDP setting, and this is the first analysis in the model-free setting that establishes $\sqrt{T}$ regret without requiring access to a "simulator."
Proceedings Article
A Unified Probabilistic Model for Global and Local Unsupervised Feature Selection
TL;DR: This paper presents a unified probabilistic model that can perform both global and local feature selection for clustering, based on a hierarchical beta-Bernoulli prior combined with a Dirichlet process mixture model.
Journal Article
Extensions of the informative vector machine
TL;DR: In this paper, the informative vector machine (IVM) is extended to a block-diagonal covariance matrix, which allows the IVM to be applied to a mixture of labeled and unlabeled data.
Proceedings Article
Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks
TL;DR: This work proposes to avoid the infeasibility of the E step by bounding likelihoods instead of computing them exactly, and shows that the estimation of the network parameters can be made fast by performing the estimation in either of the alternative domains.
Journal ArticleDOI
Bayesian inference for queueing networks and modeling of internet services
Charles Sutton,Michael I. Jordan +1 more
TL;DR: A Bayesian perspective on queueing models in which the arrival and departure times that are not observed are treated as latent variables is developed and sampled from the posterior distribution over missing data and model parameters using Markov chain Monte Carlo.