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Stochastic Gradient Descent as Approximate Bayesian Inference

13 April 2017

Matthew D. Hoffman

Papers citing "Stochastic Gradient Descent as Approximate Bayesian Inference"

23 / 23 papers shown

Title
Reinforcement Teaching Alex Lewandowski Calarina Muslimani Dale Schuurmans Matthew E. Taylor Jun Luo 185 2 0 28 Jan 2025
Soft Condorcet Optimization for Ranking of General Agents Marc Lanctot Kate Larson Michael Kaisers Quentin Berthet I. Gemp Manfred Diaz Roberto-Rafael Maura-Rivero Yoram Bachrach Anna Koop Doina Precup 227 0 0 31 Oct 2024
Variational Stochastic Gradient Descent for Deep Neural Networks Haotian Chen Anna Kuzina Babak Esmaeili Jakub M. Tomczak 69 0 0 09 Apr 2024
The Implicit Regularization of Stochastic Gradient Flow for Least Squares Alnur Ali Yan Sun Robert Tibshirani 70 77 0 17 Mar 2020
On the Convergence of Stochastic Gradient MCMC Algorithms with High-Order Integrators Changyou Chen Nan Ding Lawrence Carin 73 162 0 21 Oct 2016
The Generalized Reparameterization Gradient Francisco J. R. Ruiz Michalis K. Titsias David M. Blei BDL 95 169 0 07 Oct 2016
Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization Changyou Chen David Carlson Zhe Gan Chunyuan Li Lawrence Carin 67 90 0 25 Dec 2015
Stochastic modified equations and adaptive stochastic gradient algorithms Qianxiao Li Cheng Tai E. Weinan 59 285 0 19 Nov 2015
Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Sampling Xiaocheng Shang Zhanxing Zhu Benedict Leimkuhler Amos J. Storkey 53 52 0 29 Oct 2015
A Complete Recipe for Stochastic Gradient MCMC Yian Ma Tianqi Chen E. Fox BDL SyDa 68 489 0 15 Jun 2015
Automatic Variational Inference in Stan A. Kucukelbir Rajesh Ranganath Andrew Gelman David M. Blei BDL 71 234 0 10 Jun 2015
Towards stability and optimality in stochastic gradient descent Panos Toulis Dustin Tran E. Airoldi 74 56 0 10 May 2015
From Averaging to Acceleration, There is Only a Step-size Nicolas Flammarion Francis R. Bach 97 139 0 07 Apr 2015
Early Stopping is Nonparametric Variational Inference D. Maclaurin David Duvenaud Ryan P. Adams BDL 89 95 0 06 Apr 2015
SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives Aaron Defazio Francis R. Bach Simon Lacoste-Julien ODL 137 1,828 0 01 Jul 2014
Stochastic Gradient Hamiltonian Monte Carlo Tianqi Chen E. Fox Carlos Guestrin BDL 114 913 0 17 Feb 2014
Black Box Variational Inference Rajesh Ranganath S. Gerrish David M. Blei DRL BDL 150 1,167 0 31 Dec 2013
Auto-Encoding Variational Bayes Diederik P. Kingma Max Welling BDL 455 16,923 0 20 Dec 2013
Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n) Francis R. Bach Eric Moulines 96 405 0 10 Jun 2013
Stochastic Variational Inference Matt Hoffman David M. Blei Chong-Jun Wang John Paisley BDL 262 2,627 0 29 Jun 2012
Fixed-Form Variational Posterior Approximation through Stochastic Linear Regression Tim Salimans David A. Knowles 122 251 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
The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo Matthew D. Hoffman Andrew Gelman 174 4,309 0 18 Nov 2011