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 491–500 of 1963 papers

TitleStatusHype
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
Latent variable model for high-dimensional point process with structured missingnessCode0
Principled Preferential Bayesian OptimizationCode0
Voronoi Candidates for Bayesian OptimizationCode0
Gaussian Process-Based Nonlinear Moving Horizon Estimation—0
Combining additivity and active subspaces for high-dimensional Gaussian process modeling—0
Decentralized Event-Triggered Online Learning for Safe Consensus of Multi-Agent Systems with Gaussian Process Regression—0
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Benchmark Results

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