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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Proceedings Article
Distance Metric Learning with Application to Clustering with Side-Information
TL;DR: This paper presents an algorithm that, given examples of similar (and, if desired, dissimilar) pairs of points in �”n, learns a distance metric over ℝn that respects these relationships.
Posted Content
Trust Region Policy Optimization
TL;DR: Trust Region Policy Optimization (TRPO) as mentioned in this paper is an iterative procedure for optimizing policies, with guaranteed monotonic improvement, which is similar to natural policy gradient methods and is effective for optimizing large nonlinear policies such as neural networks.
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An Internal Model for Sensorimotor Integration
TL;DR: A sensorimotor integration task was investigated in which participants estimated the location of one of their hands at the end of movements made in the dark and under externally imposed forces, providing direct support for the existence of an internal model.
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Optimal feedback control as a theory of motor coordination.
TL;DR: This work shows that the optimal strategy in the face of uncertainty is to allow variability in redundant (task-irrelevant) dimensions, and proposes an alternative theory based on stochastic optimal feedback control, which emerges naturally from this framework.
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An introduction to MCMC for machine learning
TL;DR: This purpose of this introductory paper is to introduce the Monte Carlo method with emphasis on probabilistic machine learning and review the main building blocks of modern Markov chain Monte Carlo simulation.