NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport

9 March 2019

Matthew Hoffman

Papers citing "NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport"

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Title
Efficiently Vectorized MCMC on Modern Accelerators Hugh Dance Pierre Glaser Peter Orbanz Ryan P. Adams 47 0 0 20 Mar 2025
Empirical evaluation of normalizing flows in Markov Chain Monte Carlo David Nabergoj Erik Štrumbelj BDL TPM 40 0 0 22 Dec 2024
Running Markov Chain Monte Carlo on Modern Hardware and Software Pavel Sountsov Colin Carroll Matthew D. Hoffman BDL 34 2 0 06 Nov 2024
Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Models Jinlin Lai Justin Domke Daniel Sheldon 26 0 0 31 Oct 2024
Hamiltonian Score Matching and Generative Flows Peter Holderrieth Yilun Xu Tommi Jaakkola 26 0 0 27 Oct 2024
Amortized Bayesian Multilevel Models Daniel Habermann Marvin Schmitt Lars Kühmichel Andreas Bulling Stefan T. Radev Paul-Christian Burkner 54 3 0 23 Aug 2024
SoftCVI: Contrastive variational inference with self-generated soft labels Daniel Ward Mark Beaumont Matteo Fasiolo BDL 45 0 0 22 Jul 2024
Quasi-Bayes meets Vines David Huk Yuanhe Zhang Mark Steel Ritabrata Dutta 32 1 0 18 Jun 2024
Ai-Sampler: Adversarial Learning of Markov kernels with involutive maps Evgenii Egorov Ricardo Valperga E. Gavves BDL GAN 29 0 0 04 Jun 2024
Markovian Flow Matching: Accelerating MCMC with Continuous Normalizing Flows A. Cabezas Louis Sharrock Christopher Nemeth 34 1 0 23 May 2024
Particle Denoising Diffusion Sampler Angus Phillips Hai-Dang Dau M. Hutchinson Valentin De Bortoli George Deligiannidis Arnaud Doucet DiffM 54 25 0 09 Feb 2024
Improved off-policy training of diffusion samplers Marcin Sendera Minsu Kim Sarthak Mittal Pablo Lemos Luca Scimeca Jarrid Rector-Brooks Alexandre Adam Yoshua Bengio Nikolay Malkin OffRL 66 17 0 07 Feb 2024
Graph-accelerated Markov Chain Monte Carlo using Approximate Samples Leo L. Duan Anirban Bhattacharya 6 1 0 25 Jan 2024
Channelling Multimodality Through a Unimodalizing Transport: Warp-U Sampler and Stochastic Bridge Sampling Fei Ding David E. Jones Shiyuan He Xiao-Li Meng OT 17 0 0 01 Jan 2024
Transition Path Sampling with Boltzmann Generator-based MCMC Moves Michael Plainer Hannes Stärk Charlotte Bunne Stephan Günnemann 20 5 0 08 Dec 2023
Quantifying the effectiveness of linear preconditioning in Markov chain Monte Carlo Max Hird Samuel Livingstone 20 5 0 08 Dec 2023
Improving Gradient-guided Nested Sampling for Posterior Inference Pablo Lemos Nikolay Malkin Will Handley Yoshua Bengio Y. Hezaveh Laurence Perreault Levasseur BDL 39 9 0 06 Dec 2023
Energy-Guided Continuous Entropic Barycenter Estimation for General Costs Alexander Kolesov Petr Mokrov Igor Udovichenko Milena Gazdieva G. Pammer Anastasis Kratsios Evgeny Burnaev Alexander Korotin OT 31 2 0 02 Oct 2023
Self-Tuning Hamiltonian Monte Carlo for Accelerated Sampling H. Christiansen Federico Errica Francesco Alesiani 40 6 0 24 Sep 2023
Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics Kyle Cranmer G. Kanwar S. Racanière Danilo Jimenez Rezende P. Shanahan AI4CE 18 27 0 03 Sep 2023
Learning variational autoencoders via MCMC speed measures Marcel Hirt Vasileios Kreouzis P. Dellaportas BDL DRL 16 2 0 26 Aug 2023
Field-Level Inference with Microcanonical Langevin Monte Carlo Adrian E Bayer U. Seljak Chirag Modi 28 9 0 18 Jul 2023
Balanced Training of Energy-Based Models with Adaptive Flow Sampling Louis Grenioux Eric Moulines Marylou Gabrié 13 2 0 01 Jun 2023
Normalizing flow sampling with Langevin dynamics in the latent space Florentin Coeurdoux N. Dobigeon P. Chainais DRL 13 7 0 20 May 2023
Efficient Multimodal Sampling via Tempered Distribution Flow Yixuan Qiu Xiao Wang OT 34 2 0 08 Apr 2023
On Sampling with Approximate Transport Maps Louis Grenioux Alain Durmus Eric Moulines Marylou Gabrié OT 22 15 0 09 Feb 2023
Automatically Marginalized MCMC in Probabilistic Programming Jinlin Lai Javier Burroni Hui Guan Daniel Sheldon 16 3 0 01 Feb 2023
Microcanonical Hamiltonian Monte Carlo Jakob Robnik G. Luca E. Silverstein U. Seljak 19 14 0 16 Dec 2022
Latent Space Diffusion Models of Cryo-EM Structures Karsten Kreis Tim Dockhorn Zihao Li Ellen D. Zhong DiffM 27 15 0 25 Nov 2022
Aspects of scaling and scalability for flow-based sampling of lattice QCD Ryan Abbott M. S. Albergo Aleksandar Botev D. Boyda Kyle Cranmer ... Ali Razavi Danilo Jimenez Rezende F. Romero-López P. Shanahan Julian M. Urban 24 33 0 14 Nov 2022
Transport Reversible Jump Proposals L. Davies Roberto Salomone Matthew Sutton Christopher C. Drovandi BDL 19 1 0 22 Oct 2022
Transport Elliptical Slice Sampling A. Cabezas Christopher Nemeth 11 8 0 19 Oct 2022
Reconstructing the Universe with Variational self-Boosted Sampling Chirag Modi Yin Li David M. Blei 11 8 0 28 Jun 2022
Wide Bayesian neural networks have a simple weight posterior: theory and accelerated sampling Jiri Hron Roman Novak Jeffrey Pennington Jascha Narain Sohl-Dickstein UQCV BDL 40 6 0 15 Jun 2022
Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference R. Grumitt B. Dai U. Seljak BDL 24 12 0 27 May 2022
Principal Manifold Flows Edmond Cunningham Adam D. Cobb Susmit Jha DRL 11 7 0 14 Feb 2022
Transport Score Climbing: Variational Inference Using Forward KL and Adaptive Neural Transport Liyi Zhang David M. Blei C. A. Naesseth 22 6 0 03 Feb 2022
Path Integral Sampler: a stochastic control approach for sampling Qinsheng Zhang Yongxin Chen DiffM 13 101 0 30 Nov 2021
Bootstrap Your Flow Laurence Illing Midgley Vincent Stimper G. Simm José Miguel Hernández-Lobato 17 5 0 22 Nov 2021
Density Ratio Estimation via Infinitesimal Classification Kristy Choi Chenlin Meng Yang Song Stefano Ermon 14 38 0 22 Nov 2021
Local-Global MCMC kernels: the best of both worlds S. Samsonov E. Lagutin Marylou Gabrié Alain Durmus A. Naumov Eric Moulines 11 13 0 04 Nov 2021
Hamiltonian Dynamics with Non-Newtonian Momentum for Rapid Sampling Greg Ver Steeg Aram Galstyan 28 13 0 03 Nov 2021
Entropy-based adaptive Hamiltonian Monte Carlo Marcel Hirt Michalis K. Titsias P. Dellaportas BDL 29 7 0 27 Oct 2021
Adaptation of the Independent Metropolis-Hastings Sampler with Normalizing Flow Proposals James A. Brofos Marylou Gabrié Marcus A. Brubaker Roy R. Lederman 17 8 0 25 Oct 2021
Towards Understanding the Generative Capability of Adversarially Robust Classifiers Yao Zhu Jiacheng Ma Jiacheng Sun Zewei Chen Rongxin Jiang Zhenguo Li AAML 13 21 0 20 Aug 2021
Solution of Physics-based Bayesian Inverse Problems with Deep Generative Priors Dhruv V. Patel Deep Ray Assad A. Oberai AI4CE 11 37 0 06 Jul 2021
Reparameterized Sampling for Generative Adversarial Networks Yifei Wang Yisen Wang Jiansheng Yang Zhouchen Lin GAN 11 5 0 01 Jul 2021
Learning Equivariant Energy Based Models with Equivariant Stein Variational Gradient Descent P. Jaini Lars Holdijk Max Welling 33 11 0 15 Jun 2021
Semi-Empirical Objective Functions for MCMC Proposal Optimization Chris Cannella Vahid Tarokh 20 1 0 03 Jun 2021
Deep Learning Hamiltonian Monte Carlo Sam Foreman Xiao-Yong Jin James C. Osborn 17 16 0 07 May 2021