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1505.02827
Cited By
On Markov chain Monte Carlo methods for tall data
11 May 2015
Rémi Bardenet
Arnaud Doucet
Chris Holmes
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Papers citing
"On Markov chain Monte Carlo methods for tall data"
50 / 145 papers shown
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Running Markov Chain Monte Carlo on Modern Hardware and Software
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Diffusion Generative Modelling for Divide-and-Conquer MCMC
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20 May 2024
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Jacob A. Johnson
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Coreset Markov Chain Monte Carlo
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Trevor Campbell
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Mathias Trabs
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Emanuel Aldea
Gianni Franchi
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288
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Miguel Biron-Lattes
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Minibatch training of neural network ensembles via trajectory sampling
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Luke Causer
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21 Apr 2023
Bayesian Pseudo-Coresets via Contrastive Divergence
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20 Mar 2023
Bayesian Quantification with Black-Box Estimators
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Paweł Czyż
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117
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17 Feb 2023
Introducing Variational Inference in Statistics and Data Science Curriculum
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Jingchen Hu
220
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03 Jan 2023
Pigeonhole Stochastic Gradient Langevin Dynamics for Large Crossed Mixed Effects Models
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Cheng Li
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18 Dec 2022
PAC-Bayes Bounds for Bandit Problems: A Survey and Experimental Comparison
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David Reeb
M. Kandemir
Jan Peters
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29 Nov 2022
Non-reversible Parallel Tempering for Deep Posterior Approximation
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Wei Deng
Qian Zhang
Qi Feng
F. Liang
Guang Lin
236
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20 Nov 2022
Data Subsampling for Bayesian Neural Networks
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M. Holzmann
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204
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Approximate Methods for Bayesian Computation
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Unbiased time-average estimators for Markov chains
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Computing Bayes: From Then 'Til Now'
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David T. Frazier
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How to Combine Variational Bayesian Networks in Federated Learning
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Kadir Burak Buldu
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273
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The convergent Indian buffet process
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An optimal transport approach for selecting a representative subsample with application in efficient kernel density estimation
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Jingyi Zhang
Cheng Meng
Jun Yu
Mengrui Zhang
Wenxuan Zhong
Ping Ma
OT
205
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Bayesian inference via sparse Hamiltonian flows
Neural Information Processing Systems (NeurIPS), 2022
Na Chen
Zuheng Xu
Trevor Campbell
265
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11 Mar 2022
Variational Inference with Locally Enhanced Bounds for Hierarchical Models
International Conference on Machine Learning (ICML), 2022
Tomas Geffner
Justin Domke
217
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08 Mar 2022
Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference
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Luca Rendsburg
Agustinus Kristiadi
Philipp Hennig
U. V. Luxburg
151
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07 Mar 2022
Optimality in Noisy Importance Sampling
Signal Processing (Signal Process.), 2022
F. Llorente
Luca Martino
Jesse Read
D. Delgado
227
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Approximating Bayes in the 21st Century
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G. Martin
David T. Frazier
Christian P. Robert
223
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Bounding Wasserstein distance with couplings
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Lester W. Mackey
322
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Optimal friction matrix for underdamped Langevin sampling
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N. Kantas
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G. Pavliotis
138
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Asynchronous and Distributed Data Augmentation for Massive Data Settings
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Kshitij Khare
Sanvesh Srivastava
177
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Revealing the Distributional Vulnerability of Discriminators by Implicit Generators
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LongBing Cao
Kun-Yu Lin
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A fast asynchronous MCMC sampler for sparse Bayesian inference
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Liwei Wang
143
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