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Nonconvex sampling with the Metropolis-adjusted Langevin algorithm

By Oren Mangoubi and Nisheeth Vishnoi
The Langevin Markov chain algorithms are widely deployed methods to sample from distributions in challenging high-dimensional and non-convex statistics and machine learning applications. Despite this, current bounds for the Langevin algorithms are slower than those of competing algorithms in many important situations, for instance when sampling from weakly log-concave distributions,... Show more
April 9, 2019
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Nonconvex sampling with the Metropolis-adjusted Langevin algorithm
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