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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 261–270 of 1963 papers

TitleStatusHype
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
Forecasting intermittent time series with Gaussian Processes and Tweedie likelihood—0
Daily Land Surface Temperature Reconstruction in Landsat Cross-Track Areas Using Deep Ensemble Learning With Uncertainty Quantification—0
Provable Quantum Algorithm Advantage for Gaussian Process QuadratureCode0
Experiment Design with Gaussian Process Regression with Applications to Chance-Constrained Control—0
Robust Optimization with Diffusion Models for Green Security—0
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Benchmark Results

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