MCMC using Hamiltonian dynamics

9 June 2012

Papers citing "MCMC using Hamiltonian dynamics"

50 / 1,031 papers shown

Title
Nonlinear input design as optimal control of a Hamiltonian system Jack Umenberger Thomas B. Schon 9 5 0 06 Mar 2019
Statistical Guarantees for the Robustness of Bayesian Neural Networks L. Cardelli M. Kwiatkowska Luca Laurenti Nicola Paoletti A. Patané Matthew Wicker AAML 15 54 0 05 Mar 2019
Scalable optimization-based sampling on function space Johnathan M. Bardsley Tiangang Cui Youssef Marzouk Zheng Wang 25 17 0 03 Mar 2019
Variational Auto-Decoder: A Method for Neural Generative Modeling from Incomplete Data Amir Zadeh Y. Lim Paul Pu Liang Louis-Philippe Morency DRL 14 16 0 03 Mar 2019
Predictive Uncertainty Quantification with Compound Density Networks Agustinus Kristiadi Sina Daubener Asja Fischer BDL UQCV 25 16 0 04 Feb 2019
Understanding MCMC Dynamics as Flows on the Wasserstein Space Chang-Shu Liu Jingwei Zhuo Jun Zhu 11 22 0 01 Feb 2019
Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation Samuel Wiqvist Pierre-Alexandre Mattei Umberto Picchini J. Frellsen BDL 20 32 0 29 Jan 2019
Hamiltonian Monte-Carlo for Orthogonal Matrices V. Yanush D. Kropotov 11 1 0 23 Jan 2019
Precision Annealing Monte Carlo Methods for Statistical Data Assimilation: Metropolis-Hastings Procedures Adrian S. Wong Kangbo Hao Zheng Fang H. Abarbanel 17 1 0 14 Jan 2019
Accelerated Flow for Probability Distributions Amirhossein Taghvaei P. Mehta 21 30 0 10 Jan 2019
Bayesian calibration and sensitivity analysis for a karst aquifer model using active subspaces Mario Teixeira Parente D. Bittner S. Mattis G. Chiogna B. Wohlmuth 13 27 0 10 Jan 2019
Learning Nonlinear State Space Models with Hamiltonian Sequential Monte Carlo Sampler Duo Xu 13 2 0 03 Jan 2019
Adversarial Learning of a Sampler Based on an Unnormalized Distribution Chunyuan Li Ke Bai Jianqiao Li Guoyin Wang Changyou Chen Lawrence Carin 6 10 0 03 Jan 2019
Divergence Triangle for Joint Training of Generator Model, Energy-based Model, and Inference Model Tian Han Erik Nijkamp Xiaolin Fang Mitch Hill Song-Chun Zhu Ying Nian Wu 22 67 0 28 Dec 2018
GaussianProcesses.jl: A Nonparametric Bayes package for the Julia Language Jamie Fairbrother Christopher Nemeth M. Rischard Johanni Brea Thomas Pinder GP VLM 6 24 0 21 Dec 2018
Inference with Hamiltonian Sequential Monte Carlo Simulators Remi Daviet 12 10 0 19 Dec 2018
Breaking Reversibility Accelerates Langevin Dynamics for Global Non-Convex Optimization Xuefeng Gao Mert Gurbuzbalaban Lingjiong Zhu 11 30 0 19 Dec 2018
Bayesian deep neural networks for low-cost neurophysiological markers of Alzheimer's disease severity W. Fruehwirt Adam D. Cobb M. Mairhofer L. Weydemann H. Garn ... M. Waser D. Grossegger Pengfei Zhang Georg Dorffner Stephen J. Roberts BDL OOD 14 18 0 12 Dec 2018
Phenotype Inference with Semi-Supervised Mixed Membership Models Victor Alfonso Rodriguez A. Perotte 14 4 0 07 Dec 2018
Enhancing Perceptual Attributes with Bayesian Style Generation Aliaksandr Siarohin Gloria Zen N. Sebe Elisa Ricci EGVM GAN 16 4 0 03 Dec 2018
Tree-Structured Recurrent Switching Linear Dynamical Systems for Multi-Scale Modeling Josue Nassar Scott W. Linderman M. Bugallo Il-Su Park AI4CE 6 73 0 29 Nov 2018
Stable Tensor Neural Networks for Rapid Deep Learning Elizabeth Newman L. Horesh H. Avron M. Kilmer 29 35 0 15 Nov 2018
A Review of automatic differentiation and its efficient implementation C. Margossian 12 301 0 12 Nov 2018
Efficient Marginalization-based MCMC Methods for Hierarchical Bayesian Inverse Problems A. Saibaba Johnathan M. Bardsley D. Brown A. Alexanderian 6 12 0 02 Nov 2018
Probabilistic Programming with Densities in SlicStan: Efficient, Flexible and Deterministic Maria I. Gorinova Andrew D. Gordon Charles Sutton 17 23 0 02 Nov 2018
Leveraging Gaussian Process and Voting-Empowered Many-Objective Evaluation for Fault Identification Pei Cao Q. Shuai Jiong Tang 12 0 0 29 Oct 2018
An Efficient Implementation of Riemannian Manifold Hamiltonian Monte Carlo for Gaussian Process Models Ulrich Paquet Marco Fraccaro 16 4 0 28 Oct 2018
Bayesian cosmic density field inference from redshift space dark matter maps E. G. P. Bos F. Kitaura R. Weygaert 14 14 0 11 Oct 2018
Faster Hamiltonian Monte Carlo by Learning Leapfrog Scale Changye Wu Julien Stoehr Christian P. Robert 13 15 0 10 Oct 2018
PADDIT: Probabilistic Augmentation of Data using Diffeomorphic Image Transformation Mauricio Orbes-Arteaga Lauge Sørensen M. Jorge Cardoso Marc Modat Sebastien Ourselin Stefan Sommer Mads Nielsen Christian Igel A. Pai DiffM MedIm 11 3 0 03 Oct 2018
Approximation and sampling of multivariate probability distributions in the tensor train decomposition S. Dolgov K. Anaya-Izquierdo C. Fox Robert Scheichl 20 72 0 02 Oct 2018
Global Convergence of Stochastic Gradient Hamiltonian Monte Carlo for Non-Convex Stochastic Optimization: Non-Asymptotic Performance Bounds and Momentum-Based Acceleration Xuefeng Gao Mert Gurbuzbalaban Lingjiong Zhu 17 60 0 12 Sep 2018
Probabilistic approach to limited-data computed tomography reconstruction Z. Purisha Carl Jidling Niklas Wahlström Simo Särkkä Thomas B. Schon 20 19 0 11 Sep 2018
A Hybrid Scan Gibbs Sampler for Bayesian Models with Latent Variables Grant Backlund J. Hobert Yeun Ji Jung Kshitij Khare 11 2 0 27 Aug 2018
Adaptive Tuning Of Hamiltonian Monte Carlo Within Sequential Monte Carlo Alexander K. Buchholz Nicolas Chopin Pierre E. Jacob 19 32 0 23 Aug 2018
Unbiased Implicit Variational Inference Michalis K. Titsias Francisco J. R. Ruiz BDL 16 52 0 06 Aug 2018
Subsampling MCMC - An introduction for the survey statistician M. Quiroz M. Villani Robert Kohn Minh-Ngoc Tran Khue-Dung Dang 6 23 0 23 Jul 2018
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model A. G. Baydin Lukas Heinrich W. Bhimji Lei Shao Saeid Naderiparizi ... Philip Torr Victor W. Lee P. Prabhat Kyle Cranmer Frank D. Wood 13 31 0 20 Jul 2018
Approximate Collapsed Gibbs Clustering with Expectation Propagation Christopher Aicher E. Fox 20 0 0 19 Jul 2018
Adaptive Dimension Reduction to Accelerate Infinite-Dimensional Geometric Markov Chain Monte Carlo Shiwei Lan 19 10 0 15 Jul 2018
Surrogate-Based Bayesian Inverse Modeling of the Hydrological System: An Adaptive Approach Considering Surrogate Approximation Error Jiangjiang Zhang Q. Zheng Dingjiang Chen Laosheng Wu L. Zeng 17 36 0 10 Jul 2018
Discrete Sampling using Semigradient-based Product Mixtures Alkis Gotovos S. Hassani Andreas Krause Stefanie Jegelka 19 2 0 04 Jul 2018
Understanding and Accelerating Particle-Based Variational Inference Chang-rui Liu Jingwei Zhuo Pengyu Cheng Ruiyi Zhang Jun Zhu Lawrence Carin 9 14 0 04 Jul 2018
Bayesian Spatial Analysis of Hardwood Tree Counts in Forests via MCMC R. Entezari P. Brown Jeffrey S. Rosenthal 13 4 0 03 Jul 2018
Efficient data augmentation for multivariate probit models with panel data: An application to general practitioner decision-making about contraceptives Vincent Chin David Gunawan D. Fiebig Robert Kohn S. Sisson 11 4 0 19 Jun 2018
Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo Marton Havasi José Miguel Hernández-Lobato J. J. Murillo-Fuentes BDL 11 96 0 14 Jun 2018
Meta-Learning for Stochastic Gradient MCMC Wenbo Gong Yingzhen Li José Miguel Hernández-Lobato BDL 16 44 0 12 Jun 2018
Adaptive MCMC via Combining Local Samplers K. Shaloudegi András Gyorgy 16 1 0 11 Jun 2018
A Stein variational Newton method Gianluca Detommaso Tiangang Cui Alessio Spantini Youssef Marzouk Robert Scheichl 55 114 0 08 Jun 2018
Dynamically rescaled Hamiltonian Monte Carlo for Bayesian Hierarchical Models T. S. Kleppe 32 11 0 06 Jun 2018