MCMC using Hamiltonian dynamics

9 June 2012

Papers citing "MCMC using Hamiltonian dynamics"

50 / 1,031 papers shown

Title
On the convergence of dynamic implementations of Hamiltonian Monte Carlo and No U-Turn Samplers Alain Durmus Samuel Gruffaz Miika Kailas E. Saksman M. Vihola 17 5 0 07 Jul 2023
Spatiotemporal Besov Priors for Bayesian Inverse Problems Shiwei Lan M. Pasha Shuyi Li Weining Shen 14 5 0 28 Jun 2023
Push: Concurrent Probabilistic Programming for Bayesian Deep Learning Daniel Huang Christian Camaño Jonathan Tsegaye Jonathan Austin Gale AI4CE 28 0 0 10 Jun 2023
Entropy-based Training Methods for Scalable Neural Implicit Sampler Weijian Luo Boya Zhang Zhihua Zhang 21 10 0 08 Jun 2023
Solution of physics-based inverse problems using conditional generative adversarial networks with full gradient penalty Deep Ray Javier Murgoitio-Esandi Agnimitra Dasgupta Assad A. Oberai GAN 26 13 0 08 Jun 2023
Structured Voronoi Sampling Afra Amini Li Du Ryan Cotterell DiffM 22 1 0 05 Jun 2023
Large-Batch, Iteration-Efficient Neural Bayesian Design Optimization Navid Ansari Hans-Peter Seidel Vahid Babaei 11 2 0 01 Jun 2023
A Probabilistic Relaxation of the Two-Stage Object Pose Estimation Paradigm Onur Beker 3DV 6 0 0 01 Jun 2023
R-VGAL: A Sequential Variational Bayes Algorithm for Generalised Linear Mixed Models Bao Anh Vu David Gunawan A. Zammit‐Mangion DRL 10 1 0 01 Jun 2023
Efficient Training of Energy-Based Models Using Jarzynski Equality D. Carbone Mengjian Hua Simon Coste Eric Vanden-Eijnden 8 4 0 30 May 2023
Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo Haque Ishfaq Qingfeng Lan Pan Xu A. R. Mahmood Doina Precup Anima Anandkumar Kamyar Azizzadenesheli BDL OffRL 18 20 0 29 May 2023
Provably Fast Finite Particle Variants of SVGD via Virtual Particle Stochastic Approximation Aniket Das Dheeraj M. Nagaraj 25 7 0 27 May 2023
Improving Neural Additive Models with Bayesian Principles Kouroche Bouchiat Alexander Immer Hugo Yèche Gunnar Rätsch Vincent Fortuin BDL MedIm 26 6 0 26 May 2023
Non-Log-Concave and Nonsmooth Sampling via Langevin Monte Carlo Algorithms Tim Tsz-Kit Lau Han Liu T. Pock 34 4 0 25 May 2023
Learning Rate Free Sampling in Constrained Domains Louis Sharrock Lester W. Mackey Christopher Nemeth 28 2 0 24 May 2023
Deep Learning-enabled MCMC for Probabilistic State Estimation in District Heating Grids Andreas Bott Tim Janke Florian Steinke 13 8 0 24 May 2023
Optimal Preconditioning and Fisher Adaptive Langevin Sampling Michalis K. Titsias 19 11 0 23 May 2023
Subsampling Error in Stochastic Gradient Langevin Diffusions Kexin Jin Chenguang Liu J. Latz 20 0 0 23 May 2023
Improving Multimodal Joint Variational Autoencoders through Normalizing Flows and Correlation Analysis Agathe Senellart Clément Chadebec S. Allassonnière DRL 30 1 0 19 May 2023
Model-based Validation as Probabilistic Inference Harrison Delecki Anthony Corso Mykel J. Kochenderfer 16 7 0 17 May 2023
To smooth a cloud or to pin it down: Guarantees and Insights on Score Matching in Denoising Diffusion Models Francisco Vargas Teodora Reu A. Kerekes Michael M Bronstein DiffM 35 1 0 16 May 2023
Robustness of Bayesian ordinal response model against outliers via divergence approach Tomotaka Momozaki Tomoyuki Nakagawa 15 1 0 12 May 2023
Using a Bayesian-Inference Approach to Calibrating Models for Simulation in Robotics H. Unjhawala Ruochun Zhang Weihua Hu Jinlong Wu R. Serban Dan Negrut 15 3 0 11 May 2023
Object based Bayesian full-waveform inversion for shear elastography A. Carpio E. Cebrián Andrea Gutierrez 15 1 0 11 May 2023
CosmoPower-JAX: high-dimensional Bayesian inference with differentiable cosmological emulators Davide Piras A. Spurio Mancini 11 12 0 10 May 2023
A local resampling trick for focused molecular dynamics Josh Fass Forrest York M. Wittmann Joseph W. Kaus Yutong Zhao 17 0 0 09 May 2023
Bayesian Synthetic Likelihood David T. Frazier Christopher C. Drovandi David J. Nott 25 217 0 09 May 2023
Inferring Covariance Structure from Multiple Data Sources via Subspace Factor Analysis N. K. Chandra David B. Dunson Jason Xu CML 11 7 0 06 May 2023
A Generative Modeling Framework for Inferring Families of Biomechanical Constitutive Laws in Data-Sparse Regimes Minglang Yin Zongren Zou Enrui Zhang C. Cavinato J. Humphrey George Karniadakis SyDa MedIm AI4CE 45 11 0 04 May 2023
Mixtures of Gaussian process experts based on kernel stick-breaking processes Yuji Saikai Khue-Dung Dang 12 0 0 26 Apr 2023
Score-Based Diffusion Models as Principled Priors for Inverse Imaging Berthy T. Feng Jamie Smith Michael Rubinstein Huiwen Chang Katherine L. Bouman William T. Freeman DiffM 74 87 0 23 Apr 2023
Likelihood-Based Generative Radiance Field with Latent Space Energy-Based Model for 3D-Aware Disentangled Image Representation Y. Zhu Jianwen Xie Ping Li MedIm 18 4 0 16 Apr 2023
Bayesian Inference for Jump-Diffusion Approximations of Biochemical Reaction Networks Derya Altıntan Bastian Alt Heinz Koeppl 8 0 0 13 Apr 2023
When does Metropolized Hamiltonian Monte Carlo provably outperform Metropolis-adjusted Langevin algorithm? Yuansi Chen Khashayar Gatmiry 78 15 0 10 Apr 2023
A Comprehensive Survey on Knowledge Distillation of Diffusion Models Weijian Luo DiffM MedIm 38 33 0 09 Apr 2023
A Simple Proof of the Mixing of Metropolis-Adjusted Langevin Algorithm under Smoothness and Isoperimetry Yuansi Chen Khashayar Gatmiry 11 6 0 08 Apr 2023
Efficient Multimodal Sampling via Tempered Distribution Flow Yixuan Qiu Xiao Wang OT 34 2 0 08 Apr 2023
Conservative objective models are a special kind of contrastive divergence-based energy model Christopher Beckham C. Pal 14 4 0 07 Apr 2023
EGC: Image Generation and Classification via a Diffusion Energy-Based Model Qiushan Guo Chuofan Ma Yi-Xin Jiang Zehuan Yuan Yizhou Yu Ping Luo DiffM 12 6 0 04 Apr 2023
Diffusion Bridge Mixture Transports, Schrödinger Bridge Problems and Generative Modeling Stefano Peluchetti OT DiffM 11 47 0 03 Apr 2023
Bayesian neural networks via MCMC: a Python-based tutorial Rohitash Chandra Royce Chen Joshua Simmons BDL 24 10 0 02 Apr 2023
Fluctuation without dissipation: Microcanonical Langevin Monte Carlo Jakob Robnik U. Seljak 41 6 0 31 Mar 2023
Hessian-informed Hamiltonian Monte Carlo for high-dimensional problems M. Karimi K. Dayal M. Pozzi 16 2 0 28 Mar 2023
Soy: An Efficient MILP Solver for Piecewise-Affine Systems Haoze Wu Min Wu Dorsa Sadigh Clark W. Barrett 28 0 0 23 Mar 2023
Sampling from a Gaussian distribution conditioned on the level set of a piecewise affine, continuous function Jesse Windle 23 0 0 21 Mar 2023
Bayesian Pseudo-Coresets via Contrastive Divergence Piyush Tiwary Kumar Shubham V. Kashyap Prathosh A.P. 21 3 0 20 Mar 2023
Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review Sibo Cheng César Quilodrán-Casas Said Ouala A. Farchi Che Liu ... Weiping Ding Yike Guo A. Carrassi Marc Bocquet Rossella Arcucci AI4CE 24 124 0 18 Mar 2023
Methods and applications of PDMP samplers with boundary conditions J. Bierkens Sebastiano Grazzi Gareth O. Roberts Moritz Schauer 19 7 0 14 Mar 2023
Diverse 3D Hand Gesture Prediction from Body Dynamics by Bilateral Hand Disentanglement Xingqun Qi Chen Liu Muyi Sun Lincheng Li Changjie Fan Xin Yu SLR 65 15 0 03 Mar 2023
Mean-Square Analysis of Discretized Itô Diffusions for Heavy-tailed Sampling Ye He Tyler Farghly Krishnakumar Balasubramanian Murat A. Erdogdu 34 4 0 01 Mar 2023