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 681–690 of 1963 papers

TitleStatusHype
Bayesian Layers: A Module for Neural Network Uncertainty—0
Differentiating the multipoint Expected Improvement for optimal batch design—0
Bayesian Kernel Shaping for Learning Control—0
Analytical results for uncertainty propagation through trained machine learning regression models—0
Differentially Private Regression and Classification with Sparse Gaussian Processes—0
Differentially Private Gaussian Processes—0
Diffusion-BBO: Diffusion-Based Inverse Modeling for Online Black-Box Optimization—0
Bayesian Kernelized Tensor Factorization as Surrogate for Bayesian Optimization—0
Analytical Results for the Error in Filtering of Gaussian Processes—0
Dialogue manager domain adaptation using Gaussian process reinforcement learning—0
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Benchmark Results

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