Fetching the paper…
Reading the bibliography…
The use of Bayesian methods in large-scale data settings is attractive because of the rich hierarchical models, uncertainty quantification, and prior specification they provide.
Bayesian variable selection in linear regression
T. J. Mitchell and J. J. Beauchamp · 1988
Earlier work this paper cites.
Variable selection via Gibbs sampling
E. I. George and R. E. McCulloch · 1993
Earlier work this paper cites.
An Introduction to Computational Learning Theory
M. J. Kearns and U. Vazirani · 1994
Earlier work this paper cites.
Exponential convergence of Langevin distributions and their discrete approximations
G. O. Roberts and R. L. Tweedie · 1996
Earlier work this paper cites.
An adaptive Metropolis algorithm
H. Haario, E. Saksman, and J. Tamminen · 2001
Earlier work this paper cites.
Optimal scaling for various Metropolis-Hastings algorithms
G. O. Roberts and J. S. Rosenthal · 2001
Earlier work this paper cites.
Likelihood-based data squashing: A modeling approach to instance construction
D. Madigan, N. Raghavan, W. Dumouchel, M. Nason, C. Posse, and G. Ridgeway · 2002
Earlier work this paper cites.
Geometric approximation via coresets
P. K. Agarwal, S. Har-Peled, and K. R. Varadarajan · 2005
Earlier work this paper cites.
k-means++: The advantages of careful seeding
D. Arthur and S. Vassilvitskii · 2007
Earlier work this paper cites.
A weakly informative default prior distribution for logistic and other regression models
A. Gelman, A. Jakulin, M. G. Pittau, and Y.-S. Su · 2008
Earlier work this paper cites.
A unified framework for approximating and clustering data
D. Feldman and M. Langberg · 2011
Earlier work this paper cites.
Scalable training of mixture models via coresets
D. Feldman, M. Faulkner, and A. Krause · 2011
Earlier work this paper cites.
Concise Formulas for the Area and Volume of a Hyperspherical Cap
S. Li · 2011
Earlier work this paper cites.
Bayesian Learning via Stochastic Gradient Langevin Dynamics
M. Welling and Y. W. Teh · 2011
Cited alongside, same era.
Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring
S. Ahn, A. Korattikara, and M. Welling · 2012
Cited alongside, same era.
Concentration Inequalities: A nonasymptotic theory of independence
S. Boucheron, G. Lugosi, and P. Massart · 2013
Cited alongside, same era.
Streaming Variational Bayes
T. Broderick, N. Boyd, A. Wibisono, A. C. Wilson, and M. I. Jordan · 2013
Cited alongside, same era.
Turning big data into tiny data: Constant-size coresets for k-means, pca and projective clustering
D. Feldman, M. Schmidt, and C. Sohler · 2013
Cited alongside, same era.
Stochastic variational inference
M. D. Hoffman, D. M. Blei, C. Wang, and J. Paisley · 2013
On Markov chain Monte Carlo methods for tall data
R. Bardenet, A. Doucet, and C. C. Holmes · 2015
Later among the works it cites.
The Fundamental Incompatibility of Hamiltonian Monte Carlo and Data Subsampling
M. J. Betancourt · 2015
Later among the works it cites.
Streaming, Distributed Variational Inference for Bayesian Nonparametrics
T. Campbell, J. Straub, J. W. Fisher, III, and J. P. How · 2015
Later among the works it cites.
Variational consensus Monte Carlo
M. Rabinovich, E. Angelino, and M. I. Jordan · 2015
Later among the works it cites.
WASP: Scalable Bayes via barycenters of subset posteriors
S. Srivastava, V. Cevher, Q. Tran-Dinh, and D. Dunson · 2015
Later among the works it cites.
Noisy Monte Carlo: convergence of Markov chains with approximate transition kernels
P. Alquier, N. Friel, R. Everitt, and A. Boland · 2016
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Bayes and big data: The consensus Monte Carlo algorithm
S. L. Scott, A. W. Blocker, F. V. Bonassi, H. A. Chipman, E. I. George, and R. E. McCulloch · 2013
Cited alongside, same era.
Towards scaling up Markov chain Monte Carlo: an adaptive subsampling approach
R. Bardenet, A. Doucet, and C. C. Holmes · 2014
Cited alongside, same era.
Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget
A. Korattikara, Y. Chen, and M. Welling · 2014
Cited alongside, same era.
Firefly Monte Carlo: Exact MCMC with Subsets of Data
D. Maclaurin and R. P. Adams · 2014
Cited alongside, same era.
Ergodicity of Approximate MCMC Chains with Applications to Large Data Sets
N. S. Pillai and A. Smith · 2014
Cited alongside, same era.
Coresets for Nonparametric Estimation—the Case of DP-Means
O. Bachem, M. Lucic, and A. Krause · 2015
Cited alongside, same era.
Closest in time.
Approximate K-Means++ in Sublinear Time
O. Bachem, M. Lucic, S. H. Hassani, and A. Krause · 2016
Closest in time.
New Frameworks for Offline and Streaming Coreset Constructions
V. Braverman, D. Feldman, and H. Lang · 2016
Closest in time.
Likelihood Inflating Sampling Algorithm
R. Entezari, R. V. Craiu, and J. S. Rosenthal · 2016
Closest in time.
Local Uncertainty Sampling for Large-Scale Multi-Class Logistic Regression
L. Han, T. Yang, and T. Zhang · 2016
Closest in time.
Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures
M. Lucic, O. Bachem, and A. Krause · 2016
Closest in time.
Consistency and fluctuations for stochastic gradient Langevin dynamics
Y. W. Teh, A. H. Thiery, and S. Vollmer · 2016
Closest in time.