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 311–320 of 1963 papers

TitleStatusHype
Recommendations for Baselines and Benchmarking Approximate Gaussian Processes—0
Exact, Fast and Expressive Poisson Point Processes via Squared Neural FamiliesCode1
Neural Networks Asymptotic Behaviours for the Resolution of Inverse Problems—0
Trained quantum neural networks are Gaussian processes—0
Boundary Exploration for Bayesian Optimization With Unknown Physical ConstraintsCode0
A Novel Gaussian Min-Max Theorem and its Applications—0
Safe Active Learning for Time-Series Modeling with Gaussian Processes—0
Principled Preferential Bayesian OptimizationCode0
Latent variable model for high-dimensional point process with structured missingnessCode0
Voronoi Candidates for Bayesian OptimizationCode0
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Benchmark Results

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