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Stochastic gradient Hamiltonian Monte Carlo (SGHMC) is an efficient method for sampling from continuous distributions.
Equation of state calculations by fast computing machines
Nicholas Metropolis, Arianna W Rosenbluth, Marshall N Rosenbluth, Augusta H Teller, and Edward Teller · 1953
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Hybrid Monte Carlo
Simon Duane, Anthony D Kennedy, Brian J Pendleton, and Duncan Roweth · 1987
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A generalized guided Monte Carlo algorithm
Alan M Horowitz · 1991
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Representations of knowledge in complex systems
Ulf Grenander and Michael I Miller · 1994
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Exponential convergence of Langevin distributions and their discrete approximations
Gareth O Roberts, Richard L Tweedie, et al · 1996
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Uniform positivity improving property, Sobolev inequalities, and spectral gaps
Shigeki Aida · 1998
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Optimal scaling of discrete approximations to Langevin diffusions
Gareth O Roberts and Jeffrey S Rosenthal · 1998
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Langevin-type models i: Diffusions with given stationary distributions and their discretizations
O Stramer and RL Tweedie · 1999
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Langevin diffusions and Metropolis-Hastings algorithms
Gareth O Roberts and Osnat Stramer · 2002
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Robust stochastic approximation approach to stochastic programming
A. Nemirovski, A. Juditsky, G. Lan, and A. Shapiro · 2009
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Dirichlet forms and symmetric Markov processes , volume 19
Masatoshi Fukushima, Yoichi Oshima, and Masayoshi Takeda · 2010
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Not MNIST Dataset
Yaroslav Bulatov · 2011
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MCMC using Hamiltonian dynamics
Radford M Neal et al · 2011
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Explicit error bounds for markov chain monte carlo
Daniel Rudolf · 2011
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Irreversible Monte Carlo algorithms for efficient sampling
Konstantin S Turitsyn, Michael Chertkov, and Marija Vucelja · 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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An irreversible Markov-chain Monte Carlo method with skew detailed balance conditions
K Hukushima and Y Sakai · 2013
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Towards scaling up Markov chain Monte Carlo: an adaptive subsampling approach
Rémi Bardenet, Arnaud Doucet, and Chris Holmes · 2014
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Stochastic gradient Hamiltonian Monte Carlo
A unifying framework for devising efficient and irreversible MCMC samplers
Yi-An Ma, Tianqi Chen, Lei Wu, and Emily B Fox · 2016
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An efficient minibatch acceptance test for Metropolis-Hastings
Daniel Seita, Xinlei Pan, Haoyu Chen, and John Canny · 2016
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Exploration of the (non-) asymptotic bias and variance of stochastic gradient Langevin dynamics
Sebastian J Vollmer, Konstantinos C Zygalakis, and Yee Whye Teh · 2016
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Markov chains and mixing times , volume 107
David A Levin and Yuval Peres · 2017
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Tianqi Chen, Emily Fox, and Carlos Guestrin · 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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Spectral gaps for a Metropolis–Hastings algorithm in infinite dimensions
Martin Hairer, Stuart Andrew M., and Vollmer Sebastian J · 2014
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Austerity in MCMC land: Cutting the Metropolis-Hastings budget
Anoop Korattikara, Yutian Chen, and Max Welling · 2014
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Weight uncertainty in neural networks
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 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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Maxim Raginsky, Alexander Rakhlin, and Matus Telgarsky · 2017
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Stochastic gradient monomial gamma sampler
Yizhe Zhang, Changyou Chen, Zhe Gan, Ricardo Henao, and Lawrence Carin · 2017
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Minibatch gibbs sampling on large graphical models
Christopher De Sa, Vincent Chen, and Wing Wong · 2018
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Log-concave sampling: Metropolis-hastings algorithms are fast!
Raaz Dwivedi, Yuansi Chen, Martin J Wainwright, and Bin Yu · 2018
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Xuefeng Gao, Mert Gürbüzbalaban, and Lingjiong Zhu · 2018
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Semi-implicit variational inference
Mingzhang Yin and Mingyuan Zhou · 2018
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User-friendly guarantees for the langevin monte carlo with inaccurate gradient
Arnak S Dalalyan and Avetik Karagulyan · 2019
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Poisson-minibatching for gibbs sampling with convergence rate guarantees
Ruqi Zhang and Christopher M De Sa · 2019
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Cyclical stochastic gradient mcmc for bayesian deep learning
Ruqi Zhang, Chunyuan Li, Jianyi Zhang, Changyou Chen, and Andrew Gordon Wilson · 2020
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