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 421–430 of 1963 papers

TitleStatusHype
Federated Learning for Non-factorizable Models using Deep Generative Prior ApproximationsCode0
Minimizing UCB: a Better Local Search Strategy in Local Bayesian Optimization—0
Iterative Methods for Full-Scale Gaussian Process Approximations for Large Spatial DataCode0
Diffusion models for Gaussian distributions: Exact solutions and Wasserstein errors—0
Regression Trees Know Calculus—0
Efficient modeling of sub-kilometer surface wind with Gaussian processes and neural networks—0
Stochastic Inference of Plate Bending from Heterogeneous Data: Physics-informed Gaussian Processes via Kirchhoff-Love Theory—0
Optimal Privacy-Aware Stochastic Sampling—0
Conditionally-Conjugate Gaussian Process Factor Analysis for Spike Count Data via Data Augmentation—0
Future Aware Safe Active Learning of Time Varying Systems using Gaussian Processes—0
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Benchmark Results

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