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Gradient-based Monte Carlo sampling algorithms, like Langevin dynamics and Hamiltonian Monte Carlo, are important methods for Bayesian inference.
A stochastic approximation method
Herbert Robbins and Sutton Monro · 1951
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Hybrid monte carlo
Simon Duane, Anthony D Kennedy, Brian J Pendleton, and Duncan Roweth · 1987
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Riemann manifold langevin and hamiltonian monte carlo methods
Mark Girolami and Ben Calderhead · 2011
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Mcmc using hamiltonian dynamics
Radford M Neal et al · 2011
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Bayesian learning via stochastic gradient langevin dynamics
Max Welling and Yee W Teh · 2011
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Bayesian posterior sampling via stochastic gradient fisher scoring
Sungjin Ahn, Anoop Korattikara, and Max Welling · 2012
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Geodesic monte carlo on embedded manifolds
Simon Byrne and Mark Girolami · 2013
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Accelerating stochastic gradient descent using predictive variance reduction
Rie Johnson and Tong Zhang · 2013
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Stochastic gradient riemannian langevin dynamics on the probability simplex
Sam Patterson and Yee Whye Teh · 2013
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Distributed stochastic gradient mcmc
Sungjin Ahn, Babak Shahbaba, and Max Welling · 2014
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Stochastic gradient hamiltonian monte carlo
Tianqi Chen, Emily Fox, and Carlos Guestrin · 2014
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Saga: A fast incremental gradient method with support for non-strongly convex composite objectives
Aaron Defazio, Francis Bach, and Simon Lacoste-Julien · 2014
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Bayesian sampling using stochastic gradient thermostats
Nan Ding, Youhan Fang, Ryan Babbush, Changyou Chen, Robert D Skeel, and Hartmut Neven · 2014
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On the convergence of stochastic gradient mcmc algorithms with high-order integrators
Changyou Chen, Nan Ding, and Lawrence Carin · 2015
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A complete recipe for stochastic gradient mcmc
Yi-An Ma, Tianqi Chen, and Emily Fox · 2015
Stochastic gradient geodesic mcmc methods
Chang Liu, Jun Zhu, and Yang Song · 2016
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Stochastic variance reduction for nonconvex optimization
Sashank J Reddi, Ahmed Hefny, Suvrit Sra, Barnabás Póczos, and Alex Smola · 2016
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Katyusha: the first direct acceleration of stochastic gradient methods
Zeyuan Allen-Zhu · 2017
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A simple proximal stochastic gradient method for nonsmooth nonconvex optimization
Zhize Li and Jian Li · 2018
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Stochastic variance-reduced hamilton monte carlo methods
Difan Zou, Pan Xu, and Quanquan Gu · 2018
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Stabilized svrg: Simple variance reduction for nonconvex optimization
Rong Ge, Zhize Li, Weiyao Wang, and Xiang Wang · 2019
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Variance reduction for faster non-convex optimization
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Variance reduction in stochastic gradient langevin dynamics
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Adaptive thermostats for noisy gradient systems
Benedict Leimkuhler and Xiaocheng Shang · 2016
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