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Langevin Monte Carlo (LMC) is an iterative algorithm used to generate samples from a distribution that is known only up to a normalizing constant.
Correlation functions and computer simulations
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MCMC using Hamiltonian dynamics
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Stochastic gradient descent for non-smooth optimization: Convergence results and optimal averaging schemes
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Algorithmic theory of ODEs and sampling from well-conditioned logconcave densities
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Sparse Bayesian mass-mapping with uncertainties: Local credible intervals
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Global convergence of Langevin dynamics based algorithms for nonconvex optimization
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