J
Jon McAuliffe
Researcher at University of California, Berkeley
Publications - 59
Citations - 11577
Jon McAuliffe is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Inference & Service (business). The author has an hindex of 27, co-authored 59 publications receiving 9419 citations. Previous affiliations of Jon McAuliffe include Amazon.com & University of Pennsylvania.
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Journal ArticleDOI
Variational Inference: A Review for Statisticians
TL;DR: For instance, mean-field variational inference as discussed by the authors approximates probability densities through optimization, which is used in many applications and tends to be faster than classical methods, such as Markov chain Monte Carlo sampling.
Posted Content
Supervised Topic Models
David M. Blei,Jon McAuliffe +1 more
TL;DR: This article proposed supervised latent Dirichlet allocation (sLDA), a statistical model of labeled documents, which accommodates a variety of response types and derived an approximate maximum-likelihood procedure for parameter estimation, which relies on variational methods to handle intractable posterior expectations.
Proceedings Article
Supervised Topic Models
David M. Blei,Jon McAuliffe +1 more
TL;DR: The supervised latent Dirichlet allocation (sLDA) model, a statistical model of labelled documents, is introduced, which derives a maximum-likelihood procedure for parameter estimation, which relies on variational approximations to handle intractable posterior expectations.
Journal ArticleDOI
Convexity, Classification, and Risk Bounds
TL;DR: A general quantitative relationship between the risk as assessed using the 0–1 loss and the riskAs assessed using any nonnegative surrogate loss function is provided, and it is shown that this relationship gives nontrivial upper bounds on excess risk under the weakest possible condition on the loss function.
Journal ArticleDOI
Variational Inference: A Review for Statisticians
TL;DR: Variational inference (VI), a method from machine learning that approximates probability densities through optimization, is reviewed and a variant that uses stochastic optimization to scale up to massive data is derived.