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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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Proceedings Article

What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization?

Chi Jin, +2 more
TL;DR: A proper mathematical definition of local optimality for this sequential setting---local minimax is proposed, as well as its properties and existence results are presented.
Proceedings Article

A kernelized stein discrepancy for goodness-of-fit tests

Qiang Liu, +2 more
TL;DR: A new discrepancy statistic for measuring differences between two probability distributions is derived based on combining Stein's identity with the reproducing kernel Hilbert space theory and a new class of powerful goodness-of-fit tests are derived that are widely applicable for complex and high dimensional distributions.
Proceedings ArticleDOI

Automating model search for large scale machine learning

Evan R. Sparks, +5 more
TL;DR: An architecture for automatic machine learning at scale comprised of a cost-based cluster resource allocation estimator, advanced hyper-parameter tuning techniques, bandit resource allocation via runtime algorithm introspection, and physical optimization via batching and optimal resource allocation is proposed.
Posted Content

Near-Optimal Algorithms for Minimax Optimization

Tianyi Lin, +2 more
- 05 Feb 2020 - 
TL;DR: The current state-of-the-art first-order algorithm for strongly-convex-strongly-concave minimax problems is the algorithm of as discussed by the authors.
Proceedings ArticleDOI

Communication-Efficient Online Detection of Network-Wide Anomalies

Ling Huang, +6 more
TL;DR: This work proposes a novel approximation scheme that dramatically reduces the burden on the production network of a PCA-based anomaly detection scheme and selects the filtering parameters at local monitors such that the errors made by the detector are guaranteed to lie below a user-specified upper bound.