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Bayesian coresets approximate a posterior distribution by building a small weighted subset of the data points.
Convergence conditions for ascent methods
Philip Wolfe · 1969
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Quasi-Newton methods
Charles Broyden · 1972
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Covering the sphere by equal spherical balls
Károly Böröczky and Gergely Wintsche · 2003
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Monte Carlo Statistical Methods
Christian Robert and George Casella · 2004
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A short history of Markov Chain Monte Carlo: subjective recollections from incomplete data
Christian Robert and George Casella · 2011
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Bayesian learning via stochastic gradient Langevin dynamics
Max Welling and Yee Whye Teh · 2011
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Bayesian posterior sampling via stochastic gradient Fisher scoring
Sungjin Ahn, Anoop Korattikara, and Max Welling · 2012
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A kernel two-sample test
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
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Bayesian data analysis
Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin · 2013
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Stochastic variational inference
Matthew D. Hoffman, David M. Blei, Chong Wang, and John Paisley · 2013
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An introduction to statistical learning , volume 112
Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani · 2013
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Black box variational inference
Rajesh Ranganath, Sean Gerrish, and David Blei · 2014
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Austerity in MCMC land: cutting the Metropolis-Hastings budget
Anoop Korattikara, Yutian Chen, and Max Welling · 2014
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Firefly Monte Carlo: exact MCMC with subsets of data
Dougal Maclaurin and Ryan Adams · 2014
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The fundamental incompatibility of Hamiltonian Monte Carlo and data subsampling
Michael Betancourt · 2015
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Coresets for scalable Bayesian logistic regression
Jonathan Huggins, Trevor Campbell, and Tamara Broderick · 2016
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A kernelized Stein discrepancy for goodness-of-fit tests
Qiang Liu, Jason Lee, and Michael Jordan · 2016
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Information geometry and its applications , volume 194
Shun-ichi Amari · 2016
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The zig-zag process and super-efficient sampling for bayesian analysis of big data
Joris Bierkens, Paul Fearnhead, and Gareth Roberts · 2019
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Speeding up MCMC by efficient data subsampling
Matias Quiroz, Robert Kohn, Mattias Villani, and Minh-Ngoc Tran · 2019
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Automated scalable Bayesian inference via Hilbert coresets
Trevor Campbell and Tamara Broderick · 2019
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Sparse variational inference: Bayesian coresets from scratch
Trevor Campbell and Boyan Beronov · 2019
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Quasi-stationary monte carlo and the scale algorithm
Murray Pollock, Paul Fearnhead, Adam M Johansen, and Gareth O Roberts · 2020
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Surrogate likelihoods for variational annealed importance sampling
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Kernel Stein discrepancy descent
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Bayesian inference via sparse hamiltonian flows
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