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Further and stronger analogy between sampling and optimization: Langevin Monte Carlo and gradient descent.. COLT, том 65 из Proceedings of Machine Learning Research, стр. 678-689. PMLR, (2017)Risk bounds for aggregated shallow neural networks using Gaussian priors., и . COLT, том 178 из Proceedings of Machine Learning Research, стр. 227-253. PMLR, (2022)Sparse Regression Learning by Aggregation and Langevin Monte-Carlo., и . COLT, (2009)A New Algorithm for Estimating the Effective Dimension-Reduction Subspace., , и . J. Mach. Learn. Res., (2008)Statistical guarantees for generative models without domination., , и . ALT, том 132 из Proceedings of Machine Learning Research, стр. 1051-1071. PMLR, (2021)Penalized Langevin dynamics with vanishing penalty for smooth and log-concave targets., и . NeurIPS, (2020)Competing against the Best Nearest Neighbor Filter in Regression., и . ALT, том 6925 из Lecture Notes in Computer Science, стр. 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.Optimal detection of the feature matching map in presence of noise and outliers., , и . CoRR, (2021)Fused sparsity and robust estimation for linear models with unknown variance., и . NIPS, стр. 1268-1276. (2012)