SOTAVerified

Gaussian Processes

Gaussian Processes is a powerful framework for several machine learning tasks such as regression, classification and inference. Given a finite set of input output training data that is generated out of a fixed (but possibly unknown) function, the framework models the unknown function as a stochastic process such that the training outputs are a finite number of jointly Gaussian random variables, whose properties can then be used to infer the statistics (the mean and variance) of the function at test values of input.

Source: Sequential Randomized Matrix Factorization for Gaussian Processes: Efficient Predictions and Hyper-parameter Optimization

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 401–425 of 1963 papers

TitleStatusHype
Leave-one-out Distinguishability in Machine LearningCode0
Implicit Gaussian process representation of vector fields over arbitrary latent manifoldsCode1
Comparing Active Learning Performance Driven by Gaussian Processes or Bayesian Neural Networks for Constrained Trajectory ExplorationCode0
Tasks Makyth Models: Machine Learning Assisted Surrogates for Tipping Points—0
Neural Operator Variational Inference based on Regularized Stein Discrepancy for Deep Gaussian ProcessesCode0
Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds—0
Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds—0
Stochastic stiffness identification and response estimation of Timoshenko beams via physics-informed Gaussian processesCode0
How to turn your camera into a perfect pinhole model—0
Symbolic Regression on Sparse and Noisy Data with Gaussian Processes—0
Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian ManifoldsCode0
A spectrum of physics-informed Gaussian processes for regression in engineering—0
A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes—0
Convolutional Deep Kernel MachinesCode0
Data-driven Modeling and Inference for Bayesian Gaussian Process ODEs via Double Normalizing FlowsCode0
Modelling Irrational Behaviour of Residential End Users using Non-Stationary Gaussian Processes—0
Sparsity-Aware Distributed Learning for Gaussian Processes with Linear Multiple KernelCode0
Scalable Model-Based Gaussian Process Clustering—0
On Distributed and Asynchronous Sampling of Gaussian Processes for Sequential Binary Hypothesis Testing—0
Promises of Deep Kernel Learning for Control Synthesis—0
Bayesian Quality-Diversity approaches for constrained optimization problems with mixed continuous, discrete and categorical variables—0
Data-driven Bayesian Control of Port-Hamiltonian Systems—0
A computationally lightweight safe learning algorithm—0
CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear AlgebraCode2
Distributionally Robust Model-based Reinforcement Learning with Large State Spaces—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ICKy, periodicRoot mean square error (RMSE)0.03—Unverified