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 326–350 of 1963 papers

TitleStatusHype
Active Learning for Deep Gaussian Process SurrogatesCode0
Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural ProcessesCode0
Estimation of Dynamic Gaussian ProcessesCode0
Explainable Learning with Gaussian ProcessesCode0
Cluster-Specific Predictions with Multi-Task Gaussian ProcessesCode0
Embarrassingly Parallel Inference for Gaussian ProcessesCode0
Evaluating the squared-exponential covariance function in Gaussian processes with integral observationsCode0
Evolving-Graph Gaussian ProcessesCode0
The Debiased Spatial Whittle LikelihoodCode0
EigenGP: Gaussian Process Models with Adaptive EigenfunctionsCode0
End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control TasksCode0
Combining Pseudo-Point and State Space Approximations for Sum-Separable Gaussian ProcessesCode0
AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEsCode0
Are you sure it’s an artifact? Artifact detection and uncertainty quantification in histological imagesCode0
Comparing Active Learning Performance Driven by Gaussian Processes or Bayesian Neural Networks for Constrained Trajectory ExplorationCode0
Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian ProcessesCode0
How Good are Low-Rank Approximations in Gaussian Process Regression?Code0
A Fully Bayesian Gradient-Free Supervised Dimension Reduction Method using Gaussian ProcessesCode0
Efficiently Computable Safety Bounds for Gaussian Processes in Active LearningCode0
Federated Causal Inference from Observational DataCode0
Few-Shot Speech Deepfake Detection Adaptation with Gaussian ProcessesCode0
Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random FeaturesCode0
Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random FeaturesCode0
Compositional uncertainty in deep Gaussian processesCode0
Efficient Large-scale Nonstationary Spatial Covariance Function Estimation Using Convolutional Neural NetworksCode0
Show:102550
← PrevPage 14 of 79Next →

Benchmark Results

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