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A Complete Recipe for Stochastic Gradient MCMC

15 June 2015

Papers citing "A Complete Recipe for Stochastic Gradient MCMC"

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Title
Exponential Family Estimation via Adversarial Dynamics Embedding Bo Dai Ziqiang Liu H. Dai Niao He Arthur Gretton Le Song Dale Schuurmans 82 53 0 27 Apr 2019
Probabilistic Permutation Synchronization using the Riemannian Structure of the Birkhoff Polytope Tolga Birdal Umut Simsekli 78 38 0 11 Apr 2019
Embarrassingly parallel MCMC using deep invertible transformations Diego Mesquita P. Blomstedt Samuel Kaski 65 19 0 11 Mar 2019
On Transformations in Stochastic Gradient MCMC Soma Yokoi Takuma Otsuka Issei Sato 58 1 0 07 Mar 2019
Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning Ruqi Zhang Chunyuan Li Jianyi Zhang Changyou Chen A. Wilson BDL 88 278 0 11 Feb 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 69 78 0 04 Feb 2019
Understanding MCMC Dynamics as Flows on the Wasserstein Space Chang-Shu Liu Jingwei Zhuo Jun Zhu 106 22 0 01 Feb 2019
Non-Asymptotic Analysis of Fractional Langevin Monte Carlo for Non-Convex Optimization T. H. Nguyen Umut Simsekli G. Richard 80 28 0 22 Jan 2019
The promises and pitfalls of Stochastic Gradient Langevin Dynamics N. Brosse Alain Durmus Eric Moulines 80 78 0 25 Nov 2018
Reduced-order modeling with artificial neurons for gravitational-wave inference A. J. Chua C. Galley M. Vallisneri 69 52 0 13 Nov 2018
Global Non-convex Optimization with Discretized Diffusions Murat A. Erdogdu Lester W. Mackey Ohad Shamir 90 105 0 29 Oct 2018
Stochastic Gradient MCMC for State Space Models Christopher Aicher Yian Ma N. Foti E. Fox 64 22 0 22 Oct 2018
Deep learning with differential Gaussian process flows Pashupati Hegde Markus Heinonen Harri Lähdesmäki Samuel Kaski BDL 90 42 0 09 Oct 2018
Deterministic Variational Inference for Robust Bayesian Neural Networks Anqi Wu Sebastian Nowozin Edward Meeds Richard Turner José Miguel Hernández-Lobato Alexander L. Gaunt UQCV AAML BDL 100 16 0 09 Oct 2018
Deep convolutional Gaussian processes Kenneth Blomqvist Samuel Kaski Markus Heinonen BDL 86 61 0 06 Oct 2018
AutoLoss: Learning Discrete Schedules for Alternate Optimization Haowen Xu Huatian Zhang Zhiting Hu Xiaodan Liang Ruslan Salakhutdinov Eric Xing 78 30 0 04 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 92 60 0 12 Sep 2018
Stochastic Particle-Optimization Sampling and the Non-Asymptotic Convergence Theory Jianyi Zhang Ruiyi Zhang Lawrence Carin Changyou Chen 133 46 0 05 Sep 2018
TherML: Thermodynamics of Machine Learning Alexander A. Alemi Ian S. Fischer DRL AI4CE 58 29 0 11 Jul 2018
Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions Antoine Liutkus Umut Simsekli Szymon Majewski Alain Durmus Fabian-Robert Stöter DiffM 98 122 0 21 Jun 2018
Meta-Learning for Stochastic Gradient MCMC Wenbo Gong Yingzhen Li José Miguel Hernández-Lobato BDL 110 44 0 12 Jun 2018
Generative Modeling by Inclusive Neural Random Fields with Applications in Image Generation and Anomaly Detection Yunfu Song Zhijian Ou DiffM 107 30 0 01 Jun 2018
Bayesian Pose Graph Optimization via Bingham Distributions and Tempered Geodesic MCMC Tolga Birdal Umut Simsekli M. Eken Slobodan Ilic 85 38 0 31 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 71 167 0 04 May 2018
WHAI: Weibull Hybrid Autoencoding Inference for Deep Topic Modeling Hao Zhang Bo Chen D. Guo Mingyuan Zhou BDL 80 118 0 04 Mar 2018
Mirrored Langevin Dynamics Ya-Ping Hsieh Ali Kavis Paul Rolland Volkan Cevher 89 85 0 27 Feb 2018
On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo Niladri S. Chatterji Nicolas Flammarion Yian Ma Peter L. Bartlett Michael I. Jordan 86 87 0 15 Feb 2018
Stochastic Variance-Reduced Hamilton Monte Carlo Methods Difan Zou Pan Xu Quanquan Gu BDL 72 31 0 13 Feb 2018
On Connecting Stochastic Gradient MCMC and Differential Privacy Bai Li Changyou Chen Hao Liu Lawrence Carin 76 38 0 25 Dec 2017
Riemannian Stein Variational Gradient Descent for Bayesian Inference Chang-rui Liu Jun Zhu 65 67 0 30 Nov 2017
Stochastic gradient descent performs variational inference, converges to limit cycles for deep networks Pratik Chaudhari Stefano Soatto MLT 104 304 0 30 Oct 2017
A Convergence Analysis for A Class of Practical Variance-Reduction Stochastic Gradient MCMC Changyou Chen Wenlin Wang Yizhe Zhang Qinliang Su Lawrence Carin 92 28 0 04 Sep 2017
Differentially Private Regression for Discrete-Time Survival Analysis T. Nguyen S. Hui 48 12 0 24 Aug 2017
Hamiltonian Monte Carlo with Energy Conserving Subsampling Khue-Dung Dang M. Quiroz Robert Kohn Minh-Ngoc Tran M. Villani 120 62 0 02 Aug 2017
Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization Pan Xu Jinghui Chen Difan Zou Quanquan Gu 90 205 0 20 Jul 2017
Underdamped Langevin MCMC: A non-asymptotic analysis Xiang Cheng Niladri S. Chatterji Peter L. Bartlett Michael I. Jordan 146 302 0 12 Jul 2017
Stochastic Gradient MCMC Methods for Hidden Markov Models Yian Ma N. Foti E. Fox BDL 43 32 0 14 Jun 2017
Deep Latent Dirichlet Allocation with Topic-Layer-Adaptive Stochastic Gradient Riemannian MCMC Yulai Cong Bo Chen Hongwei Liu Mingyuan Zhou BDL 66 66 0 06 Jun 2017
Stochastic Gradient Monomial Gamma Sampler Yizhe Zhang Changyou Chen Zhe Gan Ricardo Henao Lawrence Carin BDL 74 11 0 05 Jun 2017
Geometry and Dynamics for Markov Chain Monte Carlo Alessandro Barp François‐Xavier Briol A. Kennedy Mark Girolami AI4CE 78 31 0 08 May 2017
Using Perturbed Underdamped Langevin Dynamics to Efficiently Sample from Probability Distributions A. B. Duncan Nikolas Nusken G. Pavliotis 61 41 0 29 Apr 2017
Stochastic Gradient Descent as Approximate Bayesian Inference Stephan Mandt Matthew D. Hoffman David M. Blei BDL 77 599 0 13 Apr 2017
Optimal Scaling of the MALA algorithm with Irreversible Proposals for Gaussian targets M. Ottobre Natesh S. Pillai K. Spiliopoulos 41 9 0 06 Feb 2017
Nonreversible Langevin Samplers: Splitting Schemes, Analysis and Implementation A. B. Duncan G. Pavliotis K. Zygalakis 63 25 0 16 Jan 2017
Asynchronous Stochastic Gradient MCMC with Elastic Coupling Jost Tobias Springenberg Aaron Klein Stefan Falkner Frank Hutter BDL 39 1 0 02 Dec 2016
Piecewise Deterministic Markov Processes for Continuous-Time Monte Carlo Paul Fearnhead J. Bierkens M. Pollock Gareth O. Roberts 65 108 0 23 Nov 2016
Measuring Sample Quality with Diffusions Jackson Gorham Andrew B. Duncan Sandra Jeanne Vollmer Lester W. Mackey 148 117 0 21 Nov 2016
Entropy-SGD: Biasing Gradient Descent Into Wide Valleys Pratik Chaudhari A. Choromańska Stefano Soatto Yann LeCun Carlo Baldassi C. Borgs J. Chayes Levent Sagun R. Zecchina ODL 108 775 0 06 Nov 2016
Stochastic Gradient MCMC with Stale Gradients Changyou Chen Nan Ding Chunyuan Li Yizhe Zhang Lawrence Carin BDL 104 23 0 21 Oct 2016
An Efficient Minibatch Acceptance Test for Metropolis-Hastings Daniel Seita Xinlei Pan Haoyu Chen John F. Canny 81 44 0 19 Oct 2016