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We propose a Markov chain Monte Carlo (MCMC) algorithm based on third-order Langevin dynamics for sampling from distributions with log-concave and smooth densities.
Fast mixing of Metropolized Hamiltonian Monte Carlo: Benefits of multi-step gradients
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Rapid mixing of Hamiltonian Monte Carlo on strongly log-concave distributions
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Log-concave sampling: Metropolis-Hastings algorithms are fast!
R. Dwivedi, Y. Chen, M. J. Wainwright, and B. Yu · 2018
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Algorithmic theory of ODEs and sampling from well-conditioned logconcave densities
Y.-T. Lee, Z. Song, and S. S. Vempala · 2018
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Sampling can be faster than optimization
Y.-A. Ma, Y. Chen, C. Jin, N. Flammarion, and M. I. Jordan · 2018
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Irreversible samplers from jump and continuous Markov processes
Y.-A. Ma, E. B. Fox, T. Chen, and L. Wu · 2018
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On the theory of variance reduction for stochastic gradient Monte Carlo
N. Chatterji, N. Flammarion, Y.-A. Ma, P. Bartlett, and M. Jordan · 2018
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Convergence of Langevin MCMC in KL-divergence
X. Cheng and P. L. Bartlett · 2018
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Sharp convergence rates for Langevin dynamics in the nonconvex setting
X. Cheng, N. S. Chatterji, Y. Abbasi-Yadkori, P. L. Bartlett, and M. I. Jordan · 2018
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Underdamped Langevin MCMC: A non-asymptotic analysis
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On sampling from a log-concave density using kinetic Langevin diffusions
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Dimensionally tight running time bounds for second-order Hamiltonian Monte Carlo
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User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient
A. S. Dalalyan and A. G. Karagulyan · 2019
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Is there an analog of Nesterov acceleration for MCMC?
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Complexity of randomized algorithms for underdamped langevin dynamics
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