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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Proceedings Article
High-Dimensional Continuous Control Using Generalized Advantage Estimation
TL;DR: This work addresses the large number of samples typically required and the difficulty of obtaining stable and steady improvement despite the nonstationarity of the incoming data by using value functions to substantially reduce the variance of policy gradient estimates at the cost of some bias.
Posted Content
Variational Bayesian Inference with Stochastic Search
TL;DR: This work presents an alternative algorithm based on stochastic optimization that allows for direct optimization of the variational lower bound and demonstrates the approach on two non-conjugate models: logistic regression and an approximation to the HDP.
Proceedings ArticleDOI
Kernel-based data fusion and its application to protein function prediction in yeast.
Gert R. G. Lanckriet,Minghua Deng,Nello Cristianini,Michael I. Jordan,William Stafford Noble +4 more
TL;DR: A method for combining multiple kernel representations in an optimal fashion is described, by formulating the problem as a convex optimization problem that can be solved using semidefinite programming techniques.
Proceedings ArticleDOI
Learning Semantic Correspondences with Less Supervision
TL;DR: A generative model is presented that simultaneously segments the text into utterances and maps each utterance to a meaning representation grounded in the world state and generalizes across three domains of increasing difficulty.
Journal ArticleDOI
A variational perspective on accelerated methods in optimization
TL;DR: In this article, the authors show that there is a Lagrangian functional that can generate a large class of accelerated methods in continuous time, including (but not limited to) accelerated gradient descent, its non-Euclidean extension, and accelerated higher-order gradient methods.