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In this paper, we revisit the recently established theoretical guarantees for the convergence of the Langevin Monte Carlo algorithm of sampling from a smooth and (strongly) log-concave density.
Criteria for recurrence and existence of invariant measures for multidimensional diffusions
R. N. Bhattacharya · 1978
Earlier work this paper cites.
Real and complex analysis
Walter Rudin · 1987
Earlier work this paper cites.
Convex optimization
S. Boyd and L. Vandenberghe · 2004
Earlier work this paper cites.
Fast algorithms for logconcave functions: Sampling, rounding, integration and optimization
L. Lovász and S. Vempala · 2006
Earlier work this paper cites.
Adaptive Markov chain Monte Carlo: theory and methods
Y. Atchadé, G. Fort, E. Moulines, and P. Priouret · 2011
Cited alongside, same era.
Sparse regression learning by aggregation and Langevin Monte-Carlo
A. S. Dalalyan and A. B. Tsybakov · 2012
Cited alongside, same era.
Theoretical guarantees for approximate sampling from smooth and log-concave densities
A. S. Dalalyan · 2014
Cited alongside, same era.
Hit-and-run from a corner
L. Lovász and S. Vempala
Cited in the paper.
Sampling from a log-concave distribution with Projected Langevin Monte Carlo
S. Bubeck, R. Eldan, and J. Lehec · 2015
Later among the works it cites.
High-dimensional Bayesian inference via the Unadjusted Langevin Algorithm
A. Durmus and E. Moulines · 2016
Later among the works it cites.
Sampling from convex non continuously differentiable functions, when Moreau meets Langevin
Alain Durmus, Eric Moulines, and Marcelo Pereyra · 2016
Later among the works it cites.
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