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There has been recent interest in developing scalable Bayesian sampling methods such as stochastic gradient MCMC (SG-MCMC) and Stein variational gradient descent (SVGD) for big-data analysis.
Some studies in machine learning using the game of checkers
Kolmogoroff, A. (1931) · 1931
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Equation of State Calculations by Fast Computing Machines
Metropolis, N., Rosenbluth, A., Rosenbluth, M., Teller, A., and Teller, E. (1953) · 1953
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Stochastic Differential Equations
Øksendal, B., editor (1985) · 1985
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The Fokker-Planck equation
Risken, H. (1989) · 1989
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Error analysis for implicit approximations to solutions to Cauchy problems
Rulla, J. (1996) · 1996
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The variational formulation of the Fokker-Planck equation
Jordan, R., Kinderlehrer, D., and Otto, F. (1998) · 1998
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Gradient Flows in Metric Spaces and in the Space of Probability Measures
Ambrosio, L., Gigli, N., and Savaré, G. (2005) · 2005
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Sequential Monte Carlo samplers
Moral, P. D., Doucet, A., and Jasra, A. (2006) · 2006
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The british national corpus, version 3 (bnc xml edition)
BNC Consortium, B. (2007) · 2007
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Optimal transport: old and new
Villani, C. (2008) · 2008
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Particle Markov chain Monte Carlo methods
Andrieu, C., Doucet, A., and Holenstein, R. (2010) · 2010
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Backward and Forward Equations for Diffusion Processes
Ghosh, A. P. (2011) · 2011
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Learning word vectors for sentiment analysis
Maas, A. L., Daly, R. E., Pham, P. T., Huang, D., Ng, A. Y., and Potts, C. (2011) · 2011
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Bayesian learning via stochastic gradient Langevin dynamics
Welling, M. and Teh, Y. W. (2011) · 2011
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Stochastic gradient Hamiltonian Monte Carlo
Chen, T., Fox, E. B., and Guestrin, C. (2014) · 2014
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The Exponential Formula for the Wasserstein Metric
Craig, K., editor (2014) · 2014
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Bayesian sampling using stochastic gradient thermostats
Ding, N., Fang, Y., Babbush, R., Chen, C., Skeel, R. D., and Neven, H. (2014) · 2014
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Auto-encoding variational Bayes
Kingma, D. P. and Welling, M. (2014) · 2014
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Preconditioned stochastic gradient Langevin dynamics for deep neural networks
Li, C., Chen, C., Carlson, D., and Carin, L. (2016) · 2016
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Structured and efficient variational deep learning with matrix Gaussian posteriors
Louizos, C. and Welling, M. (2016) · 2016
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Consistency and fluctuations for stochastic gradient Langevin dynamics
Teh, Y. W., Thiery, A. H., and Vollmer, S. J. (2016) · 2016
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(exploration of the (Non-)asymptotic bias and variance of stochastic gradient Langevin dynamics
Vollmer, S. J., Zygalakis, K. C., and Teh, Y. W. (2016) · 2016
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Carrillo, J. A., Craig, K., and Patacchini, F. S. (2017) · 2017
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Langevin diffusions and the Metropolis-adjusted Langevin algorithm
Xifara, T., Sherlock, C., Livingstone, S., Byrne, S., and Girolami, M. (2014) · 2014
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On the convergence of stochastic gradient MCMC algorithms with high-order integrators
Chen, C., Ding, N., and Carin, L. (2015) · 2015
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Probabilistic backpropagation for scalable learning of Bayesian neural networks
Hernández-Lobato, J. M. and Adams, R. P. (2015) · 2015
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A blob method for the aggregation equation
Craig, K. and Bertozzi, A. L. (2016) · 2016
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Provable Bayesian inference via particle mirror descent
Dai, B., He, N., Dai, H., and Song, L. (2016) · 2016
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Vime: Variational information maximizing exploration
Houthooft, R., Chen, X., Duan, Y., Schulman, J., De Turck, F., and Abbeel, P. (2016) · 2016
Cited alongside, same era.
Learning to draw samples with amortized stein variational gradient descent
Feng, Y., Wang, D., and Liu, Q. (2017) · 2017
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Reinforcement learning with deep energy-based policies
Haarnoja, T., Tang, H., Abbeel, P., and Levine, S. (2017) · 2017
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ALICE: Towards understanding adversarial learning for joint distribution matching
Li, C., Liu, H., Chen, C., Pu, Y., Chen, L., Henao, R., and Carin, L. (2017) · 2017
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Stein variational gradient descent as gradient flow
Liu, Q. (2017) · 2017
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Stein variational policy gradient
Liu, Y., Ramachandran, P., Liu, Q., and Peng, J. (2017) · 2017
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Topic compositional neural language model
Wang, W., Gan, Z., Wang, W., Shen, D., Huang, J., Ping, W., Satheesh, S., and Carin, L. (2017) · 2017
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