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Stochastic Quasi-Newton Langevin Monte Carlo

10 February 2016

Papers citing "Stochastic Quasi-Newton Langevin Monte Carlo"

38 / 38 papers shown

Title
New affine invariant ensemble samplers and their dimensional scaling Yifan Chen 308 3 0 05 May 2025
Constrained Exploration via Reflected Replica Exchange Stochastic Gradient Langevin DynamicsInternational Conference on Machine Learning (ICML), 2024 Haoyang Zheng Hengrong Du Qi Feng Wei Deng Guang Lin 185 6 0 13 May 2024
Fit Like You Sample: Sample-Efficient Generalized Score Matching from Fast Mixing DiffusionsAnnual Conference Computational Learning Theory (COLT), 2023 Yilong Qin Andrej Risteski DiffM 285 2 0 15 Jun 2023
Learning Rate Free Sampling in Constrained Domains Louis Sharrock Lester W. Mackey Christopher Nemeth 319 3 0 24 May 2023
Langevin algorithms for very deep Neural Networks with application to image classification Pierre Bras 147 6 0 27 Dec 2022
Gradient-based Adaptive Importance SamplersJournal of the Franklin Institute (JFI), 2022 Victor Elvira Émilie Chouzenoux Ömer Deniz Akyildiz Luca Martino DiffM 231 11 0 19 Oct 2022
A stochastic Stein Variational Newton method Alex Leviyev Joshua Chen Yifei Wang Omar Ghattas A. Zimmerman 145 9 0 19 Apr 2022
Sampling with Riemannian Hamiltonian Monte Carlo in a Constrained SpaceNeural Information Processing Systems (NeurIPS), 2022 Yunbum Kook Y. Lee Ruoqi Shen Santosh Vempala 227 44 0 03 Feb 2022
Batch Normalization Preconditioning for Neural Network Training Susanna Lange Kyle E. Helfrich Qiang Ye 177 12 0 02 Aug 2021
A Survey of Uncertainty in Deep Neural Networks J. Gawlikowski Cedrique Rovile Njieutcheu Tassi Mohsin Ali Jongseo Lee Matthias Humt ... R. Roscher Muhammad Shahzad Wen Yang R. Bamler Xiaoxiang Zhu BDL UQCV OOD 523 1,453 0 07 Jul 2021
Sampling with Mirrored Stein Operators Jiaxin Shi Chang-rui Liu Lester W. Mackey 334 21 0 23 Jun 2021
SNIPS: Solving Noisy Inverse Problems StochasticallyNeural Information Processing Systems (NeurIPS), 2021 Bahjat Kawar Gregory Vaksman Michael Elad 262 228 0 31 May 2021
Efficient constrained sampling via the mirror-Langevin algorithmNeural Information Processing Systems (NeurIPS), 2020 Kwangjun Ahn Sinho Chewi 291 60 0 30 Oct 2020
A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal DistributionsNeural Information Processing Systems (NeurIPS), 2020 Wei Deng Guang Lin F. Liang BDL 268 33 0 19 Oct 2020
An adaptive Hessian approximated stochastic gradient MCMC methodJournal of Computational Physics (JCP), 2020 Yating Wang Wei Deng Guang Lin BDL 84 5 0 03 Oct 2020
Non-convex Learning via Replica Exchange Stochastic Gradient MCMCInternational Conference on Machine Learning (ICML), 2020 Wei Deng Qi Feng Liyao (Mars) Gao F. Liang Guang Lin BDL 249 48 0 12 Aug 2020
Bayesian Sparse learning with preconditioned stochastic gradient MCMC and its applications Yating Wang Wei Deng Guang Lin 147 13 0 29 Jun 2020
SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergenceNeural Information Processing Systems (NeurIPS), 2020 Sinho Chewi Thibaut Le Gouic Chen Lu Tyler Maunu Philippe Rigollet 284 77 0 03 Jun 2020
Exponential ergodicity of mirror-Langevin diffusions Sinho Chewi Thibaut Le Gouic Chen Lu Tyler Maunu Philippe Rigollet Austin J. Stromme 193 53 0 19 May 2020
Newtonian Monte Carlo: single-site MCMC meets second-order gradient methods Nimar S. Arora N. Tehrani K. Shah Michael Tingley Y. Li Narjes Torabi David Noursi Sepehr Akhavan Masouleh Eric Lippert E. Meijer BDL 62 3 0 15 Jan 2020
Information Newton's flow: second-order optimization method in probability space Yifei Wang Wuchen Li 307 34 0 13 Jan 2020
Maximum entropy methods for texture synthesis: theory and practiceSIAM Journal on Mathematics of Data Science (SIMODS), 2019 Valentin De Bortoli A. Desolneux Alain Durmus B. Galerne Arthur Leclaire GAN 168 5 0 03 Dec 2019
Probabilistic Permutation Synchronization using the Riemannian Structure of the Birkhoff Polytope Tolga Birdal Umut Simsekli 211 40 0 11 Apr 2019
Non-Asymptotic Analysis of Fractional Langevin Monte Carlo for Non-Convex Optimization T. H. Nguyen Umut Simsekli G. Richard 120 30 0 22 Jan 2019
SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient Aaron Mishkin Frederik Kunstner Didrik Nielsen Mark Schmidt Mohammad Emtiyaz Khan BDL UQCV 180 65 0 11 Nov 2018
Stochastic Gradient MCMC for State Space Models Christopher Aicher Yian Ma N. Foti E. Fox 185 23 0 22 Oct 2018
Global Convergence of Stochastic Gradient Hamiltonian Monte Carlo for Non-Convex Stochastic Optimization: Non-Asymptotic Performance Bounds and Momentum-Based Acceleration Ningyuan Chen Mert Gurbuzbalaban Lingjiong Zhu 225 65 0 12 Sep 2018
Correlated pseudo-marginal Metropolis-Hastings using quasi-Newton proposals J. Dahlin A. Wills B. Ninness 132 0 0 26 Jun 2018
Asynchronous Stochastic Quasi-Newton MCMC for Non-Convex Optimization Umut Simsekli Çağatay Yıldız T. H. Nguyen G. Richard A. Cemgil 158 22 0 07 Jun 2018
Bayesian Pose Graph Optimization via Bingham Distributions and Tempered Geodesic MCMC Tolga Birdal Umut Simsekli M. Eken Slobodan Ilic 202 42 0 31 May 2018
Mirrored Langevin DynamicsNeural Information Processing Systems (NeurIPS), 2018 Ya-Ping Hsieh Ali Kavis Paul Rolland Volkan Cevher 315 90 0 27 Feb 2018
Stochastic quasi-Newton with adaptive step lengths for large-scale problems A. Wills Thomas B. Schon 130 9 0 12 Feb 2018
A Convergence Analysis for A Class of Practical Variance-Reduction Stochastic Gradient MCMC Changyou Chen Wenlin Wang Yizhe Zhang Qinliang Su Lawrence Carin 179 28 0 04 Sep 2017
Fractional Langevin Monte Carlo: Exploring Lévy Driven Stochastic Differential Equations for Markov Chain Monte CarloInternational Conference on Machine Learning (ICML), 2017 Umut Simsekli 146 46 0 12 Jun 2017
Deep Latent Dirichlet Allocation with Topic-Layer-Adaptive Stochastic Gradient Riemannian MCMCInternational Conference on Machine Learning (ICML), 2017 Yulai Cong Bo Chen Hongwei Liu Mingyuan Zhou BDL 168 68 0 06 Jun 2017
Relativistic Monte Carlo Xiaoyu Lu Valerio Perrone Leonard Hasenclever Yee Whye Teh Sebastian J. Vollmer BDL 138 39 0 14 Sep 2016
Geometric adaptive Monte Carlo in random environmentFoundations of Data Science (FODS), 2016 Theodore Papamarkou Alexey Lindo E. Ford 182 4 0 29 Aug 2016
Guaranteed inference in topic models Khoat Than Tung Doan 132 8 0 10 Dec 2015