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Theoretical guarantees for approximate sampling from smooth and log-concave densities

. (2014)cite arxiv:1412.7392Comment: To appear in JRSS B.

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Penalized Langevin dynamics with vanishing penalty for smooth and log-concave targets., and . NeurIPS, (2020)Statistical guarantees for generative models without domination., , and . ALT, volume 132 of Proceedings of Machine Learning Research, page 1051-1071. PMLR, (2021)A New Algorithm for Estimating the Effective Dimension-Reduction Subspace., , and . J. Mach. Learn. Res., (2008)Risk bounds for aggregated shallow neural networks using Gaussian priors., and . COLT, volume 178 of Proceedings of Machine Learning Research, page 227-253. PMLR, (2022)Further and stronger analogy between sampling and optimization: Langevin Monte Carlo and gradient descent.. COLT, volume 65 of Proceedings of Machine Learning Research, page 678-689. PMLR, (2017)Sparse Regression Learning by Aggregation and Langevin Monte-Carlo., and . COLT, (2009)Statistically Optimal Generative Modeling with Maximum Deviation from the Empirical Distribution., , , , and . ICML, OpenReview.net, (2024)Fused sparsity and robust estimation for linear models with unknown variance., and . NIPS, page 1268-1276. (2012)Competing against the Best Nearest Neighbor Filter in Regression., and . ALT, volume 6925 of Lecture Notes in Computer Science, page 129-143. Springer, (2011)Theoretical guarantees for approximate sampling from smooth and log-concave densities. (2014)cite arxiv:1412.7392Comment: To appear in JRSS B.