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 1351–1360 of 1963 papers

TitleStatusHype
A Generalized Unified Skew-Normal Process with Neural Bayes Inference—0
Aggregated Multi-output Gaussian Processes with Knowledge Transfer Across Domains—0
Aggregating Dependent Gaussian Experts in Local Approximation—0
A Fast and Greedy Subset-of-Data (SoD) Scheme for Sparsification in Gaussian processes—0
A Hybrid Approach for Trajectory Control Design—0
A Kernel-Based Approach for Modelling Gaussian Processes with Functional Information—0
A Learning-based Nonlinear Model Predictive Controller for a Real Go-Kart based on Black-box Dynamics Modeling through Gaussian Processes—0
Algorithmic Linearly Constrained Gaussian Processes—0
A Lifting Approach to Learning-Based Self-Triggered Control with Gaussian Processes—0
Aligned Multi-Task Gaussian Process—0
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Benchmark Results

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