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 41–50 of 1963 papers

TitleStatusHype
Bayesian Optimization of Catalysis With In-Context LearningCode1
Bayesian Optimization of Function NetworksCode1
Active Bayesian Causal InferenceCode1
Building 3D Morphable Models from a Single ScanCode1
Bayesian Deep Ensembles via the Neural Tangent KernelCode1
Batched Energy-Entropy acquisition for Bayesian OptimizationCode1
Constrained Causal Bayesian OptimizationCode1
Convergence of Sparse Variational Inference in Gaussian Processes RegressionCode1
Pre-trained Gaussian Processes for Bayesian OptimizationCode1
Bayesian Active Learning with Fully Bayesian Gaussian ProcessesCode1
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Benchmark Results

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