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On Markov chain Monte Carlo methods for tall data

On Markov chain Monte Carlo methods for tall data

11 May 2015
Rémi Bardenet
Arnaud Doucet
Chris Holmes
ArXiv (abs)PDFHTML

Papers citing "On Markov chain Monte Carlo methods for tall data"

50 / 145 papers shown
On the Collapse Errors Induced by the Deterministic Sampler for Diffusion Models
On the Collapse Errors Induced by the Deterministic Sampler for Diffusion Models
Yi Zhang
Zhenyu Liao
Jingfeng Wu
Difan Zou
DiffM
181
1
0
22 Aug 2025
Bayesian Neural Network Surrogates for Bayesian Optimization of Carbon Capture and Storage Operations
Bayesian Neural Network Surrogates for Bayesian Optimization of Carbon Capture and Storage Operations
Sofianos Panagiotis Fotias
Vassilis Gaganis
211
0
0
29 Jul 2025
Bayesian Data Sketching for Varying Coefficient Regression Models
Bayesian Data Sketching for Varying Coefficient Regression Models
Rajarshi Guhaniyogi
Laura Baracaldo
Sudipto Banerjee
140
5
0
30 May 2025
Efficient MCMC Sampling with Expensive-to-Compute and Irregular Likelihoods
Efficient MCMC Sampling with Expensive-to-Compute and Irregular Likelihoods
Conor Rosato
Harvinder Lehal
Simon Maskell
L. Devlin
Malcolm Strens
197
0
0
15 May 2025
A survey of Monte Carlo methods for noisy and costly densities with application to reinforcement learning and ABC
A survey of Monte Carlo methods for noisy and costly densities with application to reinforcement learning and ABCInternational Statistical Review (ISR), 2021
F. Llorente
Luca Martino
Jesse Read
D. Delgado
OffRL
557
16
0
03 Jan 2025
Running Markov Chain Monte Carlo on Modern Hardware and Software
Running Markov Chain Monte Carlo on Modern Hardware and Software
Pavel Sountsov
Colin Carroll
Matthew D. Hoffman
BDL
170
11
0
06 Nov 2024
Approximate Bayesian Computation with Statistical Distances for Model
  Selection
Approximate Bayesian Computation with Statistical Distances for Model Selection
Christian Angelopoulos
Clara Grazian
176
0
0
28 Oct 2024
Sampling from Bayesian Neural Network Posteriors with Symmetric Minibatch Splitting Langevin Dynamics
Sampling from Bayesian Neural Network Posteriors with Symmetric Minibatch Splitting Langevin DynamicsInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2024
Daniel Paulin
Peter Whalley
Neil K. Chada
Benedict Leimkuhler
BDL
402
6
0
14 Oct 2024
Distribution-Aware Mean Estimation under User-level Local Differential
  Privacy
Distribution-Aware Mean Estimation under User-level Local Differential Privacy
Corentin Pla
Hugo Richard
Maxime Vono
FedML
184
0
0
12 Oct 2024
Analysing symbolic data by pseudo-marginal methods
Analysing symbolic data by pseudo-marginal methods
Yu Yang
M. Quiroz
B. Beranger
Robert Kohn
Scott A. Sisson
146
0
0
08 Aug 2024
Ensemble Kalman inversion approximate Bayesian computation
Ensemble Kalman inversion approximate Bayesian computation
R. Everitt
198
0
0
26 Jul 2024
Diffusion Generative Modelling for Divide-and-Conquer MCMC
Diffusion Generative Modelling for Divide-and-Conquer MCMC
C. Trojan
Paul Fearnhead
C. Nemeth
DiffM
197
1
0
17 Jun 2024
General bounds on the quality of Bayesian coresets
General bounds on the quality of Bayesian coresets
Trevor Campbell
214
3
0
20 May 2024
Diffusion posterior sampling for simulation-based inference in tall data
  settings
Diffusion posterior sampling for simulation-based inference in tall data settings
J. Linhart
Gabriel Victorino Cardoso
Alexandre Gramfort
Sylvain Le Corff
Pedro L. C. Rodrigues
DiffM
271
12
0
11 Apr 2024
Minibatch Markov chain Monte Carlo Algorithms for Fitting Gaussian
  Processes
Minibatch Markov chain Monte Carlo Algorithms for Fitting Gaussian ProcessesBayesian Analysis (Bayes. Anal.), 2023
Matthew J. Heaton
Jacob A. Johnson
199
1
0
26 Oct 2023
Coreset Markov Chain Monte Carlo
Coreset Markov Chain Monte CarloInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Naitong Chen
Trevor Campbell
229
4
0
25 Oct 2023
Neural Likelihood Approximation for Integer Valued Time Series Data
Neural Likelihood Approximation for Integer Valued Time Series Data
Luke O'Loughlin
John Maclean
Andrew Black
AI4TS
156
0
0
19 Oct 2023
The surrogate Gibbs-posterior of a corrected stochastic MALA: Towards uncertainty quantification for neural networks
The surrogate Gibbs-posterior of a corrected stochastic MALA: Towards uncertainty quantification for neural networks
S. Bieringer
Gregor Kasieczka
Maximilian F. Steffen
Mathias Trabs
310
1
0
13 Oct 2023
A Symmetry-Aware Exploration of Bayesian Neural Network Posteriors
A Symmetry-Aware Exploration of Bayesian Neural Network PosteriorsInternational Conference on Learning Representations (ICLR), 2023
Olivier Laurent
Emanuel Aldea
Gianni Franchi
BDLUQCV
287
10
0
12 Oct 2023
Enhancing Sample Quality through Minimum Energy Importance Weights
Enhancing Sample Quality through Minimum Energy Importance Weights
Chaofan Huang
V. R. Joseph
246
0
0
12 Oct 2023
Pigeons.jl: Distributed Sampling From Intractable Distributions
Pigeons.jl: Distributed Sampling From Intractable DistributionsJuliaCon Proceedings (JuliaCon), 2023
Nikola Surjanovic
Miguel Biron-Lattes
P. Tiede
Saifuddin Syed
Trevor Campbell
Alexandre Bouchard-Côté
194
3
0
18 Aug 2023
Minibatch training of neural network ensembles via trajectory sampling
Minibatch training of neural network ensembles via trajectory sampling
Jamie F. Mair
Luke Causer
J. P. Garrahan
202
0
0
23 Jun 2023
Bayesian inference and neural estimation of acoustic wave propagation
Bayesian inference and neural estimation of acoustic wave propagation
Yongchao Huang
Yuhang He
Hong Ge
127
0
0
28 May 2023
Parameter estimation from an Ornstein-Uhlenbeck process with measurement
  noise
Parameter estimation from an Ornstein-Uhlenbeck process with measurement noisePhysical Review E (PRE), 2023
Simon Carter
Lilianne Mujica-Parodi
H. Strey
136
2
0
22 May 2023
Variational Inference for Bayesian Neural Networks under Model and
  Parameter Uncertainty
Variational Inference for Bayesian Neural Networks under Model and Parameter Uncertainty
A. Hubin
G. Storvik
BDLUQCV
303
6
0
01 May 2023
Machine Learning and the Future of Bayesian Computation
Machine Learning and the Future of Bayesian Computation
Steven Winter
Trevor Campbell
Lizhen Lin
Sanvesh Srivastava
David B. Dunson
TPM
315
6
0
21 Apr 2023
Bayesian Pseudo-Coresets via Contrastive Divergence
Bayesian Pseudo-Coresets via Contrastive DivergenceConference on Uncertainty in Artificial Intelligence (UAI), 2023
Piyush Tiwary
Kumar Shubham
V. Kashyap
Prathosh A.P.
254
4
0
20 Mar 2023
Bayesian Quantification with Black-Box Estimators
Bayesian Quantification with Black-Box Estimators
Albert Ziegler
Paweł Czyż
UQCV
109
3
0
17 Feb 2023
Introducing Variational Inference in Statistics and Data Science
  Curriculum
Introducing Variational Inference in Statistics and Data Science CurriculumAmerican Statistician (Am. Stat.), 2023
Vojtech Kejzlar
Jingchen Hu
209
4
0
03 Jan 2023
Pigeonhole Stochastic Gradient Langevin Dynamics for Large Crossed Mixed
  Effects Models
Pigeonhole Stochastic Gradient Langevin Dynamics for Large Crossed Mixed Effects ModelsBayesian Analysis (Bayesian Anal.), 2022
Xinyu Zhang
Cheng Li
169
0
0
18 Dec 2022
PAC-Bayes Bounds for Bandit Problems: A Survey and Experimental
  Comparison
PAC-Bayes Bounds for Bandit Problems: A Survey and Experimental ComparisonIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022
H. Flynn
David Reeb
M. Kandemir
Jan Peters
OffRL
286
7
0
29 Nov 2022
Non-reversible Parallel Tempering for Deep Posterior Approximation
Non-reversible Parallel Tempering for Deep Posterior ApproximationAAAI Conference on Artificial Intelligence (AAAI), 2022
Wei Deng
Qian Zhang
Qi Feng
F. Liang
Guang Lin
234
4
0
20 Nov 2022
Data Subsampling for Bayesian Neural Networks
Data Subsampling for Bayesian Neural Networks
Eiji Kawasaki
M. Holzmann
BDL
201
1
0
17 Oct 2022
Approximate Methods for Bayesian Computation
Approximate Methods for Bayesian ComputationAnnual Review of Statistics and Its Application (ARSIA), 2022
Radu V. Craiu
Evgeny Levi
181
5
0
06 Oct 2022
Unbiased time-average estimators for Markov chains
Unbiased time-average estimators for Markov chainsMathematics of Operations Research (MOR), 2022
N. Kahalé
167
3
0
20 Sep 2022
Deep Variational Free Energy Approach to Dense Hydrogen
Deep Variational Free Energy Approach to Dense HydrogenPhysical Review Letters (PRL), 2022
H.-j. Xie
Ziqun Li
Han Wang
Linfeng Zhang
Lei Wang
213
14
0
13 Sep 2022
Computing Bayes: From Then 'Til Now'
Computing Bayes: From Then 'Til Now'Statistical Science (Statist. Sci.), 2022
G. Martin
David T. Frazier
Christian P. Robert
240
18
0
01 Aug 2022
How to Combine Variational Bayesian Networks in Federated Learning
How to Combine Variational Bayesian Networks in Federated Learning
Atahan Ozer
Kadir Burak Buldu
Abdullah Akgul
Gözde B. Ünal
FedML
270
7
0
22 Jun 2022
The convergent Indian buffet process
The convergent Indian buffet process
Ilsang Ohn
53
0
0
16 Jun 2022
An optimal transport approach for selecting a representative subsample
  with application in efficient kernel density estimation
An optimal transport approach for selecting a representative subsample with application in efficient kernel density estimationJournal of Computational And Graphical Statistics (JCGS), 2022
Jingyi Zhang
Cheng Meng
Jun Yu
Mengrui Zhang
Wenxuan Zhong
Ping Ma
OT
197
19
0
31 May 2022
Bayesian inference via sparse Hamiltonian flows
Bayesian inference via sparse Hamiltonian flowsNeural Information Processing Systems (NeurIPS), 2022
Na Chen
Zuheng Xu
Trevor Campbell
260
14
0
11 Mar 2022
Variational Inference with Locally Enhanced Bounds for Hierarchical
  Models
Variational Inference with Locally Enhanced Bounds for Hierarchical ModelsInternational Conference on Machine Learning (ICML), 2022
Tomas Geffner
Justin Domke
209
5
0
08 Mar 2022
Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for
  Approximate Bayesian Inference
Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian InferenceInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2022
Luca Rendsburg
Agustinus Kristiadi
Philipp Hennig
U. V. Luxburg
143
2
0
07 Mar 2022
Optimality in Noisy Importance Sampling
Optimality in Noisy Importance SamplingSignal Processing (Signal Process.), 2022
F. Llorente
Luca Martino
Jesse Read
D. Delgado
221
5
0
07 Jan 2022
Approximating Bayes in the 21st Century
Approximating Bayes in the 21st CenturyStatistical Science (Statist. Sci.), 2021
G. Martin
David T. Frazier
Christian P. Robert
219
28
0
20 Dec 2021
Bounding Wasserstein distance with couplings
Bounding Wasserstein distance with couplingsJournal of the American Statistical Association (JASA), 2021
N. Biswas
Lester W. Mackey
322
8
0
06 Dec 2021
Optimal friction matrix for underdamped Langevin sampling
Optimal friction matrix for underdamped Langevin sampling
Martin Chak
N. Kantas
T. Lelièvre
G. Pavliotis
134
10
0
30 Nov 2021
Asynchronous and Distributed Data Augmentation for Massive Data Settings
Asynchronous and Distributed Data Augmentation for Massive Data Settings
Jiayuan Zhou
Kshitij Khare
Sanvesh Srivastava
177
4
0
18 Sep 2021
Revealing the Distributional Vulnerability of Discriminators by Implicit
  Generators
Revealing the Distributional Vulnerability of Discriminators by Implicit GeneratorsIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021
Zhilin Zhao
LongBing Cao
Kun-Yu Lin
432
17
0
23 Aug 2021
A fast asynchronous MCMC sampler for sparse Bayesian inference
A fast asynchronous MCMC sampler for sparse Bayesian inference
Yves F. Atchadé
Liwei Wang
138
3
0
14 Aug 2021
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