Stochastic gradient Markov Chain Monte Carlo algorithms are popular samplers for approximate inference, but they are generally biased.
Reading the bibliography…
Cyclical stochastic gradient MCMC for Bayesian deep learning
Ruqi Zhang, Chunyuan Li, Jianyi Zhang, Changyou Chen, and Andrew Gordon Wilson · 1902
Earlier work this paper cites.
Partitioned integrators for thermodynamic parameterization of neural networks
Benedict Leimkuhler, Charles Matthews, and Tiffany Vlaar · 1908
Earlier work this paper cites.
Equation of state calculations by fast computing machines
Nicholas Metropolis, Arianna W Rosenbluth, Marshall N Rosenbluth, Augusta H Teller, and Edward Teller · 1953
Earlier work this paper cites.
Monte Carlo sampling methods using Markov chains and their applications
W.K. Hastings · 1970
Earlier work this paper cites.
Hybrid Monte Carlo
Simon Duane, A.D. Kennedy, Brian J. Pendleton, and Duncan Roweth · 1987
Earlier work this paper cites.
A generalized guided Monte Carlo algorithm
Alan M. Horowitz · 1991
Earlier work this paper cites.
Practical Markov chain Monte Carlo
Charles J Geyer · 1992
Earlier work this paper cites.
Probabilistic inference using Markov chain Monte Carlo methods
Radford M. Neal · 1993
Earlier work this paper cites.
Bayesian learning for neural networks , volume 118
Radford M. Neal · 1996
Earlier work this paper cites.
Optimal scaling of discrete approximations to Langevin diffusions
Gareth O. Roberts and Jeffrey S. Rosenthal · 1998
Cited alongside, same era.
How good is the Bayes posterior in deep neural networks really?, 2020
Florian Wenzel, Kevin Roth, Bastiaan S. Veeling, Jakub Świątkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, and Sebastian Nowozin · 2002
Cited alongside, same era.
Hao Wu, Jonas Köhler, and Frank Noé · 2002
Cited alongside, same era.
Accurate sampling using Langevin dynamics
Giovanni Bussi and Michele Parrinello · 2007
Cited alongside, same era.
AMAGOLD: Amortized Metropolis adjustment for efficient stochastic gradient MCMC
Ruqi Zhang, A. Feder Cooper, and Christopher De Sa · 2007
Cited alongside, same era.
Bayesian learning via stochastic gradient Langevin dynamics
Max Welling and Yee Whye Teh · 2011
Cited alongside, same era.
MCMC using Hamiltonian dynamics
Radford M. Neal · 2012
Cited alongside, same era.
Stochastic gradient Hamiltonian Monte Carlo
Tianqi Chen, Emily Fox, and Carlos Guestrin · 2014
Cited alongside, same era.
Bayesian sampling using stochastic gradient thermostats
Nan Ding, Youhan Fang, Ryan Babbush, Changyou Chen, Robert D Skeel, and Hartmut Neven · 2014
Later among the works it cites.
Time step rescaling recovers continuous-time dynamical properties for discrete-time Langevin integration of nonequilibrium systems
David A. Sivak, John D. Chodera, and Gavin E. Crooks · 2014
Later among the works it cites.
Hamiltonian monte carlo without detailed balance
Jascha Sohl-Dickstein, Mayur Mudigonda, and Michael DeWeese · 2014
Later among the works it cites.
Numerical Methods for Stochastic Molecular Dynamics , chapter 7, pages 261–328
Ben Leimkuhler and Charles Matthews · 2015
Later among the works it cites.
Jug: Software for parallel reproducible computation in python
Luis Pedro Coelho · 2017
Later among the works it cites.
Langevin Markov chain Monte Carlo with stochastic gradients
Charles Matthews and Jonathan Weare · 2018
Later among the works it cites.
Irreversible samplers from jump and continuous markov processes
Yi-An Ma, Emily Fox, and Tianqi Chen · 2019
Later among the works it cites.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
Later among the works it cites.
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