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As an important Markov Chain Monte Carlo (MCMC) method, stochastic gradient Langevin dynamics (SGLD) algorithm has achieved great success in Bayesian learning and posterior sampling.
Correlation functions and computer simulations
G Parisi · 1981
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Mimicking the one-dimensional marginal distributions of processes having an itô differential
István Gyöngy · 1986
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Diffusion for global optimization in rˆn
Tzuu-Shuh Chiang, Chii-Ruey Hwang, and Shuenn Jyi Sheu · 1987
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Numerical Solution of Stochastic Differential Equations
P. Kloeden abd E. Platen · 1992
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Matrix Computation
Gene Golub and Charles Van Loan · 1996
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On the trend to equilibrium for the fokker-planck equation: an interplay between physics and functional analysis
Peter A Markowich and Cédric Villani · 1999
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Ergodicity for sdes and approximations: locally lipschitz vector fields and degenerate noise
Jonathan C Mattingly, Andrew M Stuart, and Desmond J Higham · 2002
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Metastability in reversible diffusion processes i: Sharp asymptotics for capacities and exit times
Anton Bovier, Michael Eckhoff, Véronique Gayrard, and Markus Klein · 2004
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Weighted Csiszár-Kullback-Pinsker inequalities and applications to transportation inequalities
Francois Bolley and Cedric Villani · 2005
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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 Teh Yee Whye · 2011
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Analysis and geometry of Markov diffusion operators , volume 348
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Stochastic Gradient Hamiltonian Monte Carlo
Tianqi Chen, Emily Fox, and Carlos Guestrin · 2014
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Michael Betancourt · 2015
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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
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Variance Reduction in Stochastic Gradient Langevin Dynamics
Kumar Avinava Dubey, Sashank J Reddi, Sinead A Williamson, Barnabas Poczos, Alexander J Smola, and Eric P Xing · 2016
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Preconditioned stochastic gradient langevin dynamics for deep neural networks
Chunyuan Li, Changyou Chen, David Carlson, and Carin Lawrence · 2016
Non-convex finite-sum optimization via scsg methods
Lihua Lei, Cheng Ju, Jianbo Chen, and Michael I Jordan · 2017
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Generalization bounds of sgld for non-convex learning: Two theoretical viewpoints
Wenlong Mou, Liwei Wang, Xiyu Zhai, and Kai Zheng · 2017
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Non-convex learning via stochastic gradient Langevin dynamics: a nonasymptotic analysis
Maxim Raginsky, Alexander Rakhlin, and Matus Telgarsky · 2017
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On the theory of variance reduction for stochastic gradient monte carlo
Niladri S Chatterji, Nicolas Flammarion, Yi-An Ma, Peter L Bartlett, and Michael I Jordan · 2018
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Stochastic Gradient Hamiltonian Monte Carlo with Variance Reduction for Bayesian Inference
Zhize Li, Tianyi Zhang, and Jian Li · 2018
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Yee Whye Teh, Alexandre H Thiery, and Sebastian J Vollmer · 2016
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