A Unifying and Canonical Description of Measure-Preserving Diffusions

6 May 2021

Papers citing "A Unifying and Canonical Description of Measure-Preserving Diffusions"

41 / 41 papers shown

Title
Targeted Separation and Convergence with Kernel Discrepancies Alessandro Barp Carl-Johann Simon-Gabriel Mark Girolami Lester W. Mackey 109 15 0 26 Sep 2022
Constraint-Based Regularization of Neural Networks Benedict Leimkuhler Timothée Pouchon Tiffany J. Vlaar Amos Storkey 30 10 0 17 Jun 2020
Deep Markov Chain Monte Carlo Babak Shahbaba L. M. Lomeli T. Chen Shiwei Lan BDL 47 8 0 13 Oct 2019
Minimum Stein Discrepancy Estimators Alessandro Barp François‐Xavier Briol Andrew B. Duncan Mark Girolami Lester W. Mackey 66 93 0 19 Jun 2019
Stein Point Markov Chain Monte Carlo W. Chen Alessandro Barp François‐Xavier Briol Jackson Gorham Mark Girolami Lester W. Mackey Chris J. Oates 84 57 0 09 May 2019
Irreversible Langevin MCMC on Lie Groups Alexis Arnaudon Alessandro Barp So Takao AI4CE 32 5 0 21 Mar 2019
Hamiltonian Monte Carlo on Symmetric and Homogeneous Spaces via Symplectic Reduction Alessandro Barp A. Kennedy Mark Girolami 27 9 0 07 Mar 2019
Is There an Analog of Nesterov Acceleration for MCMC? Yian Ma Niladri Chatterji Xiang Cheng Nicolas Flammarion Peter L. Bartlett Michael I. Jordan BDL 58 78 0 04 Feb 2019
Global Non-convex Optimization with Discretized Diffusions Murat A. Erdogdu Lester W. Mackey Ohad Shamir 75 105 0 29 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 60 60 0 12 Sep 2018
An elementary introduction to information geometry Frank Nielsen 3DGS 51 221 0 17 Aug 2018
Note on the geodesic Monte Carlo Andrew J Holbrook 13 2 0 14 May 2018
Sharp convergence rates for Langevin dynamics in the nonconvex setting Xiang Cheng Niladri S. Chatterji Yasin Abbasi-Yadkori Peter L. Bartlett Michael I. Jordan 52 166 0 04 May 2018
Analysis of Langevin Monte Carlo via convex optimization Alain Durmus Szymon Majewski B. Miasojedow 64 222 0 26 Feb 2018
Riemannian Stein Variational Gradient Descent for Bayesian Inference Chang-rui Liu Jun Zhu 55 67 0 30 Nov 2017
Geometric integrators and the Hamiltonian Monte Carlo method Nawaf Bou-Rabee J. Sanz-Serna 58 98 0 14 Nov 2017
Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization Pan Xu Jinghui Chen Difan Zou Quanquan Gu 81 205 0 20 Jul 2017
Underdamped Langevin MCMC: A non-asymptotic analysis Xiang Cheng Niladri S. Chatterji Peter L. Bartlett Michael I. Jordan 92 301 0 12 Jul 2017
Modified Hamiltonian Monte Carlo for Bayesian inference Tijana Radivojević E. Akhmatskaya 89 31 0 13 Jun 2017
Deep Latent Dirichlet Allocation with Topic-Layer-Adaptive Stochastic Gradient Riemannian MCMC Yulai Cong Bo Chen Hongwei Liu Mingyuan Zhou BDL 61 66 0 06 Jun 2017
Geometry and Dynamics for Markov Chain Monte Carlo Alessandro Barp François‐Xavier Briol A. Kennedy Mark Girolami AI4CE 58 31 0 08 May 2017
On the convergence of Hamiltonian Monte Carlo Alain Durmus Eric Moulines E. Saksman 70 70 0 29 Apr 2017
Monte Carlo on manifolds: sampling densities and integrating functions Emilio Zappa Miranda C. Holmes-Cerfon J. Goodman 38 67 0 26 Feb 2017
Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis Maxim Raginsky Alexander Rakhlin Matus Telgarsky 75 521 0 13 Feb 2017
Nonreversible Langevin Samplers: Splitting Schemes, Analysis and Implementation A. B. Duncan G. Pavliotis K. Zygalakis 53 25 0 16 Jan 2017
Geodesic Lagrangian Monte Carlo over the space of positive definite matrices: with application to Bayesian spectral density estimation Andrew J Holbrook Shiwei Lan A. Vandenberg-Rodes Babak Shahbaba 57 26 0 24 Dec 2016
Measuring Sample Quality with Diffusions Jackson Gorham Andrew B. Duncan Sandra Jeanne Vollmer Lester W. Mackey 73 117 0 21 Nov 2016
Bayesian Inference on Matrix Manifolds for Linear Dimensionality Reduction Andrew J Holbrook A. Vandenberg-Rodes Babak Shahbaba 35 15 0 14 Jun 2016
A Kernelized Stein Discrepancy for Goodness-of-fit Tests and Model Evaluation Qiang Liu Jason D. Lee Michael I. Jordan 108 486 0 10 Feb 2016
On the Geometric Ergodicity of Hamiltonian Monte Carlo Samuel Livingstone M. Betancourt Simon Byrne Mark Girolami 104 116 0 29 Jan 2016
High-Order Stochastic Gradient Thermostats for Bayesian Learning of Deep Models Chunyuan Li Changyou Chen Kai Fan Lawrence Carin BDL 78 25 0 23 Dec 2015
A Complete Recipe for Stochastic Gradient MCMC Yian Ma Tianqi Chen E. Fox BDL SyDa 73 489 0 15 Jun 2015
Measuring Sample Quality with Stein's Method Jackson Gorham Lester W. Mackey 122 225 0 09 Jun 2015
Hamiltonian Monte Carlo Without Detailed Balance Jascha Narain Sohl-Dickstein M. Mudigonda M. DeWeese 77 66 0 18 Sep 2014
Information-geometric Markov Chain Monte Carlo methods using Diffusions Samuel Livingstone Mark Girolami DiffM 98 45 0 31 Mar 2014
Compressible Generalized Hybrid Monte Carlo Youhan Fang J. Sanz-Serna R. Skeel 73 61 0 28 Feb 2014
Stochastic Gradient Hamiltonian Monte Carlo Tianqi Chen E. Fox Carlos Guestrin BDL 119 913 0 17 Feb 2014
Langevin diffusions and the Metropolis-adjusted Langevin algorithm Tatiana Xifara Chris Sherlock Samuel Livingstone Simon Byrne Mark Girolami 82 126 0 11 Sep 2013
Geodesic Monte Carlo on Embedded Manifolds Simon Byrne Mark Girolami 118 152 0 25 Jan 2013
Sampling From A Manifold P. Diaconis Susan P. Holmes M. Shahshahani 104 116 0 28 Jun 2012
Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring S. Ahn Anoop Korattikara Balan Max Welling 77 306 0 27 Jun 2012