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 1671–1680 of 1963 papers

TitleStatusHype
Gaussian Process Accelerated Feldman-Cousins Approach for Physical Parameter Inference—0
Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces—0
Efficient Spatio-Temporal Gaussian Regression via Kalman Filtering—0
Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification—0
Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systems—0
Emerging Statistical Machine Learning Techniques for Extreme Temperature Forecasting in U.S. Cities—0
Emulating dynamic non-linear simulators using Gaussian processes—0
Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE)—0
Meta-models for transfer learning in source localisation—0
End-to-End Learning of Deep Kernel Acquisition Functions for Bayesian Optimization—0
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Benchmark Results

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