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 1051–1060 of 1963 papers

TitleStatusHype
Scalable Gaussian Processes with Low-Rank Deep Kernel Decomposition—0
Scalable Gaussian Process Hyperparameter Optimization via Coverage Regularization—0
Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees—0
Scalable Gaussian Process Regression for Kernels with a Non-Stationary Phase—0
Scalable Inference for Nonparametric Hawkes Process Using Pólya-Gamma Augmentation—0
Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction—0
Scalable Levy Process Priors for Spectral Kernel Learning—0
Scalable Machine Learning Algorithms using Path Signatures—0
Scalable Meta-Learning with Gaussian Processes—0
Scalable Model-Based Gaussian Process Clustering—0
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Benchmark Results

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