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The Underdamped Langevin Monte Carlo (ULMC) is a popular Markov chain Monte Carlo sampling method.
Non-asymptotic results for langevin monte carlo: Coordinate-wise and black-box sampling
Shen, L., Balasubramanian, K., and Ghadimi, S. (2019) · 1902
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On explicit L 2 L^{2} -convergence rate estimate for underdamped Langevin dynamics
Cao, Y., Lu, J., and Wang, L. (2019) · 1908
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Ensemble Kalman inversion: mean-field limit and convergence analysis
Ding, Z. and Li, Q. (2019a) · 1908
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Ensemble Kalman sampler: mean-field limit and convergence analysis
Ding, Z. and Li, Q. (2019b) · 1910
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Monte Carlo sampling methods using Markov chains and their applications
Hastings, W. (1970) · 1970
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Brownian dynamics as smart Monte Carlo simulation
Rossky, P. J., Doll, J. D., and Friedman, H. L. (1978) · 1978
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Correlation functions and computer simulations
Parisi, G. (1981) · 1981
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Bayesian inference in econometric models using Monte Carlo integration
Geweke, J. (1989) · 1989
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Exponential convergence of Langevin distributions and their discrete approximations
Roberts, G. and Tweedie, R. (1996) · 1996
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Sequential Monte Carlo methods in practice
Doucet, A., Freitas, N., and Gordon, N. (2001) · 2001
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Ergodicity for sdes and approximations: locally Lipschitz vector fields and degenerate noise
Mattingly, J., Stuart, A., and Higham, D. (2002) · 2002
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An introduction to MCMC for Machine Learning
Andrieu, C., Freitas, N., Doucet, A., and Jordan, M. (2003) · 2003
Cited alongside, same era.
Ensemble Kalman inversion for nonlinear problems: weights, consistency, and variance bounds
Ding, Z., Li, Q., and Lu, J. (2020a) · 2003
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Data Assimilation: The ensemble Kalman filter
Evensen, G. (2006) · 2006
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Hypocoercivity
Villani, C. (2006) · 2006
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Hypocoercivity for kinetic equations with linear relaxation terms
Dolbeault, J., Mouhot, C., and Schmeiser, C. (2009) · 2009
Cited alongside, same era.
Random coordinate langevin monte carlo
Ding, Z., Li, Q., Lu, J., and Wright, S. J. (2020b) · 2010
Wright, S. (2015) · 2015
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Wasserstein contraction properties for hypoelliptic diffusions
Baudoin, F. (2016) · 2016
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Underdamped Langevin MCMC: a non-asymptotic analysis
Cheng, X., Chatterji, N., Bartlett, P., and Jordan, M. (2018) · 2018
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On sampling from a log-concave density using kinetic Langevin diffusions
Dalalyan, A. S. and Riou-Durand, L. (2018) · 2018
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Couplings and quantitative contraction rates for langevin dynamics
Eberle, A., Guillin, A., and Zimmer, R. (2018) · 2018
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Bayesian learning via stochastic gradient langevin dynamics
Welling, M. and Teh, Y. W. (2011) · 2011
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Diffusion limits of the random walk metropolis algorithm in high dimensions
Mattingly, J. C., Pillai, N. S., Stuart, A. M., et al. (2012) · 2012
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Efficiency of coordinate descent methods on huge-scale optimization problems
Nesterov, Y. (2012) · 2012
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Optimal scaling and diffusion limits for the langevin algorithm in high dimensions
Pillai, N. S., Stuart, A. M., Thiéry, A. H., et al. (2012) · 2012
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Ensemble Kalman methods for inverse problems
Iglesias, M., Law, K., and Stuart, A. (2013) · 2013
Cited alongside, same era.
User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient
Dalalyan, A. and Karagulyan, A. (2019) · 2019
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Analysis of Langevin Monte Carlo via convex optimization
Durmus, A., Majewski, S., and Miasojedow, B. (2019) · 2019
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Interacting Langevin diffusions: Gradient structure and Ensemble Kalman sampler
Garbuno-Inigo, A., Hoffmann, F., Li, W., and Stuart, A. (2020) · 2020
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On the ergodicity, bias and asymptotic normality of randomized midpoint sampling method
He, Y., Erdogdu, M., and Balasubramanian, K. (2020) · 2020
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The randomized midpoint method for log-concave sampling
Shen, R. and Lee, Y. T. (2019) · 2098
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