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

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Showing 426–450 of 1963 papers

TitleStatusHype
Decentralized Online Ensembles of Gaussian Processes for Multi-Agent SystemsCode0
Exact Gaussian Processes on a Million Data PointsCode0
Decomposing Gaussians with Unknown CovarianceCode0
Deconditional Downscaling with Gaussian ProcessesCode0
Adversarial Robustness Guarantees for Gaussian ProcessesCode0
Evolving-Graph Gaussian ProcessesCode0
Explainable Learning with Gaussian ProcessesCode0
Estimation of Z-Thickness and XY-Anisotropy of Electron Microscopy Images using Gaussian ProcessesCode0
Manifold Gaussian Processes for RegressionCode0
Approximate Latent Force Model InferenceCode0
Avoiding Kernel Fixed Points: Computing with ELU and GELU Infinite NetworksCode0
Evaluating the squared-exponential covariance function in Gaussian processes with integral observationsCode0
Deep convolutional Gaussian processesCode0
Boundary Exploration for Bayesian Optimization With Unknown Physical ConstraintsCode0
Adaptive Basis Function Selection for Computationally Efficient PredictionsCode0
Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active LearningCode0
Approximate Inference Turns Deep Networks into Gaussian ProcessesCode0
All your loss are belong to BayesCode0
MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under non-parameterized geometrical variabilityCode0
Evaluating Uncertainty in Deep Gaussian ProcessesCode0
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian OptimizationCode0
Entropic Trace Estimates for Log DeterminantsCode0
End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control TasksCode0
Batch Bayesian Optimization via Local PenalizationCode0
Epistemic Uncertainty in Conformal Scores: A Unified ApproachCode0
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Benchmark Results

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