The following pages link to Bayesian manifold regression (Q282481):
Displaying 33 items.
- High-dimensional intrinsic interpolation using Gaussian process regression and diffusion maps (Q1719851) (← links)
- Flexible Bayesian dynamic modeling of correlation and covariance matrices (Q2057355) (← links)
- Probabilistic modelling of general noisy multi-manifold data sets (Q2060713) (← links)
- Adaptive learning rates for support vector machines working on data with low intrinsic dimension (Q2073699) (← links)
- Semi-parametric Bayes regression with network-valued covariates (Q2102416) (← links)
- Bandit and covariate processes, with finite or non-denumerable set of arms (Q2145828) (← links)
- Kernel-based system identification with manifold regularization: a Bayesian perspective (Q2151950) (← links)
- Airflow recovery from thoracic and abdominal movements using synchrosqueezing transform and locally stationary Gaussian process regression (Q2157494) (← links)
- Multi-output regression on the output manifold (Q2270745) (← links)
- Longitudinal image analysis via path regression on the image manifold (Q2278713) (← links)
- Extrinsic Gaussian processes for regression and classification on manifolds (Q2316989) (← links)
- Probabilistic integration: a role in statistical computation? (Q2325605) (← links)
- Minimax-optimal nonparametric regression in high dimensions (Q2343958) (← links)
- Diffusion \(K\)-means clustering on manifolds: provable exact recovery via semidefinite relaxations (Q2659762) (← links)
- Estimation of a regression function on a manifold by fully connected deep neural networks (Q2676904) (← links)
- Compressed Gaussian process for manifold regression (Q2810879) (← links)
- Regression on manifolds using data-dependent regularization with applications in computer vision (Q2870760) (← links)
- (Q5011559) (← links)
- (Q5011560) (← links)
- Gaussian Process Landmarking on Manifolds (Q5025781) (← links)
- (Q5054643) (← links)
- Symmetry tests for manifold-valued random variables (Q5079020) (← links)
- Intrinsic Dimension Adaptive Partitioning for Kernel Methods (Q5089718) (← links)
- (Q5148959) (← links)
- Variational Bayesian data analysis on manifold (Q5381386) (← links)
- Can We Trust Bayesian Uncertainty Quantification from Gaussian Process Priors with Squared Exponential Covariance Kernel? (Q5858422) (← links)
- Gaussian Process Subspace Prediction for Model Reduction (Q5864693) (← links)
- Rates of the strong uniform consistency for the kernel-type regression function estimators with general kernels on manifolds (Q6044263) (← links)
- Sets that maximize probability and a related variational problem (Q6059519) (← links)
- Deep nonparametric regression on approximate manifolds: nonasymptotic error bounds with polynomial prefactors (Q6172194) (← links)
- Minimax rate of distribution estimation on unknown submanifolds under adversarial losses (Q6177323) (← links)
- A diffusion process perspective on posterior contraction rates for parameters (Q6583523) (← links)
- Solving PDEs on spheres with physics-informed convolutional neural networks (Q6652574) (← links)
