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 11–20 of 1963 papers

TitleStatusHype
Direct Integration of Recursive Gaussian Process Regression Into Extended Kalman Filters With Application to Vapor Compression Cycle Control—0
Machine learning for in-situ composition mapping in a self-driving magnetron sputtering system—0
Discovery of Probabilistic Dirichlet-to-Neumann Maps on Graphs—0
Constrained Bayesian Optimization under Bivariate Gaussian Process with Application to Cure Process Optimization—0
Adaptive finite element type decomposition of Gaussian processes—0
Few-Shot Speech Deepfake Detection Adaptation with Gaussian ProcessesCode0
Learning-Based Robust Fixed-Time Terminal Sliding Mode Control—0
A Provable Approach for End-to-End Safe Reinforcement Learning—0
STACI: Spatio-Temporal Aleatoric Conformal Inference—0
Scalable Gaussian Processes with Low-Rank Deep Kernel Decomposition—0
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Benchmark Results

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