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 161–170 of 1963 papers

TitleStatusHype
Online scalable Gaussian processes with conformal prediction for guaranteed coverage—0
Automating the Design of Multi-band Microstrip Antennas via Uniform Cross-Entropy Optimization—0
Flow Matching with Gaussian Process Priors for Probabilistic Time Series Forecasting—0
Deep Kernel Posterior Learning under Infinite Variance Prior WeightsCode0
GPTreeO: An R package for continual regression with dividing local Gaussian processes—0
Stream-level flow matching with Gaussian processesCode0
Convergence of Diffusion Models Under the Manifold Hypothesis in High-Dimensions—0
Safe Time-Varying Optimization based on Gaussian Processes with Spatio-Temporal Kernel—0
Reactive Multi-Robot Navigation in Outdoor Environments Through Uncertainty-Aware Active Learning of Human Preference Landscape—0
AUGUR, A flexible and efficient optimization algorithm for identification of optimal adsorption sites—0
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Benchmark Results

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