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 271–280 of 1963 papers

TitleStatusHype
Tensor Network-Constrained Kernel Machines as Gaussian ProcessesCode0
Learning Piecewise Residuals of Control Barrier Functions for Safety of Switching Systems using Multi-Output Gaussian Processes—0
A Unified Kernel for Neural Network Learning—0
Multi-Agent Clarity-Aware Dynamic Coverage with Gaussian Processes—0
Guided Bayesian Optimization: Data-Efficient Controller Tuning with Digital Twin—0
Deep Gaussian Covariance Network with Trajectory Sampling for Data-Efficient Policy SearchCode0
Hyperbolic Secant representation of the logistic function: Application to probabilistic Multiple Instance Learning for CT intracranial hemorrhage detectionCode0
Kernel Multigrid: Accelerate Back-fitting via Sparse Gaussian Process Regression—0
Composite likelihood estimation of stationary Gaussian processes with a view toward stochastic volatility—0
A tutorial on learning from preferences and choices with Gaussian ProcessesCode1
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Benchmark Results

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