Results 11 to 20 of about 13,863,967 (339)
Convergence Rates for the Constrained Sampling via Langevin Monte Carlo [PDF]
Entropy, 2023 Sampling from constrained distributions has posed significant challenges in terms of algorithmic design and non-asymptotic analysis, which are frequently encountered in statistical and machine-learning models.
Yuanzheng Zhu
doaj +2 more sources
Convergence Rates for Learning Linear Operators from Noisy Data [PDF]
SIAM/ASA J. Uncertain. Quantification, 2021 This paper studies the learning of linear operators between infinite-dimensional Hilbert spaces. The training data comprises pairs of random input vectors in a Hilbert space and their noisy images under an unknown self-adjoint linear operator.
M. Hoop +3 more
semanticscholar +1 more source
Convergence rates of Gaussian ODE filters. [PDF]
Stat Comput, 2020 A recently introduced class of probabilistic (uncertainty-aware) solvers for ordinary differential equations (ODEs) applies Gaussian (Kalman) filtering to initial value problems.
Kersting H, Sullivan TJ, Hennig P.
europepmc +3 more sources
An improved central limit theorem and fast convergence rates for entropic transportation costs [PDF]
SIAM Journal on Mathematics of Data Science, 2022 We prove a central limit theorem for the entropic transportation cost between subgaussian probability measures, centered at the population cost. This is the first result which allows for asymptotically valid inference for entropic optimal transport ...
E. Barrio +3 more
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Strong convergence rates in averaging principle for slow-fast McKean-Vlasov SPDEs [PDF]
Journal of Differential Equations, 2021 In this paper, we aim to study the asymptotic behaviour for a class of McKean-Vlasov stochastic partial differential equations with slow and fast time-scales.
Wei Hong, Shihu Li, Wei Liu
semanticscholar +1 more source
Optimal Convergence Rates for the Proximal Bundle Method [PDF]
SIAM Journal on Optimization, 2021 We study convergence rates of the classic proximal bundle method for a variety of nonsmooth convex optimization problems. We show that, without any modification, this algorithm adapts to converge faster in the presence of smoothness or a H\"older growth ...
M. D'iaz, Benjamin Grimmer
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Uniform Convergence Rates for Lipschitz Learning on Graphs [PDF]
arXiv.org, 2021 Lipschitz learning is a graph-based semi-supervised learning method where one extends labels from a labeled to an unlabeled data set by solving the infinity Laplace equation on a weighted graph.
Leon Bungert, J. Calder, Tim Roith
semanticscholar +1 more source
Convergence rates of deep ReLU networks for multiclass classification [PDF]
Electronic Journal of Statistics, 2021 For classification problems, trained deep neural networks return probabilities of class memberships. In this work we study convergence of the learned probabilities to the true conditional class probabilities.
Thijs Bos, J. Schmidt-Hieber
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Optimal Convergence Rates for the Orthogonal Greedy Algorithm [PDF]
IEEE Transactions on Information Theory, 2021 We analyze the orthogonal greedy algorithm when applied to dictionaries $\mathbb {D}$ whose convex hull has small entropy. We show that if the metric entropy of the convex hull of $\mathbb {D}$ decays at a rate of $O\left({n^{-\frac {1}{2}-\alpha }}\
Jonathan W. Siegel, Jinchao Xu
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Improved spectral convergence rates for graph Laplacians on ε-graphs and k-NN graphs
Applied and Computational Harmonic Analysis, 2022 In this paper we improve the spectral convergence rates for graph-based approximations of Laplace-Beltrami operators constructed from random data. We utilize regularity of the continuum eigenfunctions and strong pointwise consistency results to prove ...
J. Calder, Nicolás García Trillos
semanticscholar +1 more source