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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Minimax Optimal Procedures for Locally Private Estimation
TL;DR: Private versions of classical information-theoretical bounds, in particular those due to Le Cam, Fano, and Assouad, are developed to allow for a precise characterization of statistical rates under local privacy constraints and the development of provably (minimax) optimal estimation procedures.
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Local Privacy and Statistical Minimax Rates
TL;DR: In this paper, the tradeoff between privacy guarantees and the utility of the resulting statistical estimators was studied under local privacy constraints, and lower and upper bounds on mutual information and Kullback-Leibler divergence were established.
Posted Content
A Lyapunov Analysis of Momentum Methods in Optimization
TL;DR: There is an equivalence between the technique of estimate sequences and a family of Lyapunov functions in both continuous and discrete time, which allows for a simple and unified analysis of many existing momentum algorithms.
Proceedings Article
Nonparametric Bayesian Learning of Switching Linear Dynamical Systems
TL;DR: This work develops a sampling algorithm that combines a truncated approximation to the Dirichlet process with efficient joint sampling of the mode and state sequences in an unknown number of persistent, smooth dynamical modes.