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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 61–70 of 1963 papers

TitleStatusHype
Support Collapse of Deep Gaussian Processes with Polynomial Kernels for a Wide Regime of Hyperparameters—0
Efficient dynamic modal load reconstruction using physics-informed Gaussian processes based on frequency-sparse Fourier basis functionsCode0
Large Scale Multi-Task Bayesian Optimization with Large Language Models—0
BARK: A Fully Bayesian Tree Kernel for Black-box Optimization—0
Real-time Spatial-temporal Traversability Assessment via Feature-based Sparse Gaussian ProcessCode2
Deterministic Global Optimization of the Acquisition Function in Bayesian Optimization: To Do or Not To Do?—0
A physics-informed Bayesian optimization method for rapid development of electrical machines—0
Gaussian process surrogate model to approximate power grid simulators -- An application to the certification of a congestion management controller—0
An interpretation of the Brownian bridge as a physics-informed prior for the Poisson equation—0
Shared Stochastic Gaussian Process Latent Variable Models: A Multi-modal Generative Model for Quasar SpectraCode0
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Benchmark Results

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