Pages that link to "Item:Q3176234"

The following pages link to Uncertainty Quantification in Graph-Based Classification of High Dimensional Data (Q3176234):

Displaying 36 items.

• Minimax lower bounds for function estimation on graphs (Q1746533) (← links)
• Properly-weighted graph Laplacian for semi-supervised learning (Q2019913) (← links)
• Stochastic block models are a discrete surface tension (Q2022738) (← links)
• The SPDE approach to Matérn fields: graph representations (Q2092895) (← links)
• Graph-theoretic algorithms for Kolmogorov operators: approximating solutions and their gradients in elliptic and parabolic problems on manifolds (Q2111184) (← links)
• Improved spectral convergence rates for graph Laplacians on \(\varepsilon \)-graphs and \(k\)-NN graphs (Q2155800) (← links)
• The Bayesian update: variational formulations and gradient flows (Q2297229) (← links)
• Spectral analysis of weighted Laplacians arising in data clustering (Q2667045) (← links)
• Continuum Limits of Posteriors in Graph Bayesian Inverse Problems (Q3176426) (← links)
• Graph-based optimization approaches for machine learning, uncertainty quantification and networks (Q3295563) (← links)
• On the Well-posedness of Bayesian Inverse Problems (Q4960997) (← links)
• (Q4969066) (← links)
• Data-driven prediction of multistable systems from sparse measurements (Q5000863) (← links)
• Variational Limits of $k$-NN Graph-Based Functionals on Data Clouds (Q5025776) (← links)
• Gaussian Process Landmarking on Manifolds (Q5025781) (← links)
• Graph-based prior and forward models for inverse problems on manifolds with boundaries (Q5030164) (← links)
• A Maximum Principle Argument for the Uniform Convergence of Graph Laplacian Regressors (Q5037572) (← links)
• Lipschitz Regularity of Graph Laplacians on Random Data Clouds (Q5037712) (← links)
• Gradient flows and randomised thresholding: sparse inversion and classification* (Q5058108) (← links)
• Nonparametric Bayesian label prediction on a large graph using truncated Laplacian regularization (Q5086360) (← links)
• Multilevel approximation of Gaussian random fields: Fast simulation (Q5112023) (← links)
• Data Based Construction of Kernels for Classification (Q5118788) (← links)
• Kernel Methods for Bayesian Elliptic Inverse Problems on Manifolds (Q5139358) (← links)
• (Q5148978) (← links)
• Posterior consistency of semi-supervised regression on graphs (Q5157865) (← links)
• Stability of Gibbs Posteriors from the Wasserstein Loss for Bayesian Full Waveform Inversion (Q5158929) (← links)
• Ensemble Kalman inversion: a derivative-free technique for machine learning tasks (Q5197869) (← links)
• (Q5214228) (← links)
• Analysis of $p$-Laplacian Regularization in Semisupervised Learning (Q5231303) (← links)
• Semi-supervised learning with summary statistics (Q5236748) (← links)
• Classification and image processing with a semi‐discrete scheme for fidelity forced Allen–Cahn on graphs (Q6068273) (← links)
• Spectral gaps and error estimates for infinite-dimensional Metropolis-Hastings with non-Gaussian priors (Q6104013) (← links)
• Bayesian Inverse Problems Are Usually Well-Posed (Q6115454) (← links)
• Batch active learning for multispectral and hyperspectral image segmentation using similarity graphs (Q6575291) (← links)
• Model change active learning in graph-based semi-supervised learning (Q6575306) (← links)
• On the Laplace equation on bounded subanalytic manifolds (Q6636313) (← links)