Asymptotically Exact, Embarrassingly Parallel MCMC

19 November 2013

Papers citing "Asymptotically Exact, Embarrassingly Parallel MCMC"

50 / 123 papers shown

Title
Stacking Variational Bayesian Monte Carlo Francesco Silvestrin Chengkun Li Luigi Acerbi BDL 42 0 0 07 Apr 2025
Capture Global Feature Statistics for One-Shot Federated Learning Zenghao Guan Yucan Zhou Xiaoyan Gu FedML 63 0 0 10 Mar 2025
Adaptive posterior distributions for uncertainty analysis of covariance matrices in Bayesian inversion problems for multioutput signals E. Curbelo Luca Martino F. Llorente D. Delgado-Gomez 42 1 0 03 Jan 2025
Data value estimation on private gradients Zijian Zhou Xinyi Xu Daniela Rus Bryan Kian Hsiang Low 74 0 0 22 Dec 2024
Diffusion Generative Modelling for Divide-and-Conquer MCMC C. Trojan Paul Fearnhead C. Nemeth DiffM 23 1 0 17 Jun 2024
Embarrassingly Parallel GFlowNets Tiago da Silva Luiz Max Carvalho Amauri Souza Samuel Kaski Diego Mesquita 42 1 0 05 Jun 2024
One-Shot Federated Learning with Bayesian Pseudocoresets Tim d'Hondt Mykola Pechenizkiy Robert Peharz FedML 34 0 0 04 Jun 2024
Enhancing Transfer Learning with Flexible Nonparametric Posterior Sampling Hyungi Lee G. Nam Edwin Fong Juho Lee BDL 32 5 0 12 Mar 2024
Parallelized Midpoint Randomization for Langevin Monte Carlo Lu Yu A. Dalalyan 19 6 0 22 Feb 2024
Distributed Markov Chain Monte Carlo Sampling based on the Alternating Direction Method of Multipliers Alexandros E. Tzikas Licio Romao Mert Pilanci Alessandro Abate Mykel J. Kochenderfer 26 0 0 29 Jan 2024
Calibrated One Round Federated Learning with Bayesian Inference in the Predictive Space Mohsin Hasan Guojun Zhang Kaiyang Guo Xi Chen Pascal Poupart FedML 29 9 0 15 Dec 2023
Efficiently analyzing large patient registries with Bayesian joint models for longitudinal and time-to-event data P. M. Afonso D. Rizopoulos A. Palipana G. C. Zhou C. Brokamp R. Szczesniak E. Andrinopoulou 11 3 0 05 Oct 2023
A Primer on Bayesian Neural Networks: Review and Debates Federico Danieli Konstantinos Pitas M. Vladimirova Vincent Fortuin BDL AAML 56 18 0 28 Sep 2023
Machine Learning and the Future of Bayesian Computation Steven Winter Trevor Campbell Lizhen Lin Sanvesh Srivastava David B. Dunson TPM 33 4 0 21 Apr 2023
Federated Variational Inference Methods for Structured Latent Variable Models Conor Hassan Roberto Salomone Kerrie Mengersen BDL FedML 16 4 0 07 Feb 2023
Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms Vincent Plassier Alain Durmus Eric Moulines FedML 14 6 0 31 Oct 2022
Approximate Methods for Bayesian Computation Radu V. Craiu Evgeny Levi 13 5 0 06 Oct 2022
SwISS: A Scalable Markov chain Monte Carlo Divide-and-Conquer Strategy Callum Vyner Christopher Nemeth Chris Sherlock 14 26 0 08 Aug 2022
Computing Bayes: From Then 'Til Now' G. Martin David T. Frazier Christian P. Robert 22 15 0 01 Aug 2022
Robust One Round Federated Learning with Predictive Space Bayesian Inference Mohsin Hasan Zehao Zhang Kaiyang Guo Mahdi Karami Guojun Zhang Xi Chen Pascal Poupart FedML OOD 17 1 0 20 Jun 2022
Distributed data analytics Richard Mortier Hamed Haddadi S. S. Rodríguez Liang Wang 21 2 0 26 Mar 2022
Parallel MCMC Without Embarrassing Failures Daniel Augusto R. M. A. de Souza Diego Mesquita Samuel Kaski Luigi Acerbi 36 11 0 22 Feb 2022
Spatial meshing for general Bayesian multivariate models M. Peruzzi David B. Dunson 88 6 0 25 Jan 2022
On Convergence of Federated Averaging Langevin Dynamics Wei Deng Qian Zhang Yi-An Ma Zhao-quan Song Guang Lin FedML 27 16 0 09 Dec 2021
The divide-and-conquer sequential Monte Carlo algorithm: theoretical properties and limit theorems Juan Kuntz F. R. Crucinio A. M. Johansen 19 11 0 29 Oct 2021
Divide-and-Conquer Fusion Ryan S.Y. Chan M. Pollock A. M. Johansen Gareth O. Roberts 19 2 0 14 Oct 2021
A Survey of Monte Carlo Methods for Parameter Estimation D. Luengo Luca Martino M. Bugallo Victor Elvira S. Särkkä 14 153 0 25 Jul 2021
DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs Vincent Plassier Maxime Vono Alain Durmus Eric Moulines 6 17 0 11 Jun 2021
QLSD: Quantised Langevin stochastic dynamics for Bayesian federated learning Maxime Vono Vincent Plassier Alain Durmus Aymeric Dieuleveut Eric Moulines FedML 22 35 0 01 Jun 2021
MCMC-driven importance samplers F. Llorente E. Curbelo Luca Martino Victor Elvira D. Delgado 27 11 0 06 May 2021
Divide-and-Conquer MCMC for Multivariate Binary Data Suchit Mehrotra H. Brantley P. Onglao Patricia Bata R. Romero Jacob Westman L. Bangerter A. Maity 6 2 0 17 Feb 2021
Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms Maruan Al-Shedivat Jennifer Gillenwater Eric P. Xing Afshin Rostamizadeh FedML 14 109 0 11 Oct 2020
Parallelizing MCMC Sampling via Space Partitioning V. Hafych P. Eller O. Schulz Allen Caldwel 26 4 0 07 Aug 2020
A Decentralized Approach to Bayesian Learning Anjaly Parayil H. Bai Jemin George Prudhvi K. Gurram 14 2 0 14 Jul 2020
A Survey of Bayesian Statistical Approaches for Big Data Farzana Jahan Insha Ullah Kerrie Mengersen 37 14 0 08 Jun 2020
Informed Proposal Monte Carlo Sarouyeh Khoshkholgh A. Zunino K. Mosegaard 6 14 0 29 May 2020
Non-reversible guided Metropolis kernel K. Kamatani Xiaolin Song 6 1 0 12 May 2020
Federated Stochastic Gradient Langevin Dynamics Khaoula El Mekkaoui Diego Mesquita P. Blomstedt Samuel Kaski FedML 16 24 0 23 Apr 2020
Computing Bayes: Bayesian Computation from 1763 to the 21st Century G. Martin David T. Frazier Christian P. Robert 40 17 0 14 Apr 2020
A High-Performance Implementation of Bayesian Matrix Factorization with Limited Communication T. Aa Xiangju Qin P. Blomstedt Roel Wuyts Wilfried Verachtert Samuel Kaski 11 0 0 06 Apr 2020
Variational Inference with Vine Copulas: An efficient Approach for Bayesian Computer Model Calibration Vojtech Kejzlar T. Maiti 6 6 0 28 Mar 2020
Parallelising MCMC via Random Forests Changye Wu Christian P. Robert 4 5 0 21 Nov 2019
An Algorithm for Distributed Bayesian Inference in Generalized Linear Models N. Shyamalkumar Sanvesh Srivastava 10 0 0 18 Nov 2019
Machine Learning Systems for Highly-Distributed and Rapidly-Growing Data Kevin Hsieh SyDa OOD 9 4 0 18 Oct 2019
Distributed Computation for Marginal Likelihood based Model Choice Alexander K. Buchholz Daniel Ahfock S. Richardson FedML 12 5 0 10 Oct 2019
The Non-IID Data Quagmire of Decentralized Machine Learning Kevin Hsieh Amar Phanishayee O. Mutlu Phillip B. Gibbons 6 555 0 01 Oct 2019
Mini-batch Metropolis-Hastings MCMC with Reversible SGLD Proposal Tung-Yu Wu Y. X. R. Wang W. Wong 17 10 0 08 Aug 2019
Stochastic gradient Markov chain Monte Carlo Christopher Nemeth Paul Fearnhead BDL 16 135 0 16 Jul 2019
Consensus Monte Carlo for Random Subsets using Shared Anchors Yang Ni Yuan Ji P. Müller 14 14 0 28 Jun 2019
Analyzing and Storing Network Intrusion Detection Data using Bayesian Coresets: A Preliminary Study in Offline and Streaming Settings Fabio Massimo Zennaro 6 6 0 20 Jun 2019