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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 211–220 of 1963 papers

TitleStatusHype
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
Unsupervised cell segmentation by fast Gaussian ProcessesCode0
Predictive posterior sampling from non-stationnary Gaussian process priors via Diffusion models with application to climate dataCode0
Multi-Output Gaussian Processes for Graph-Structured DataCode0
Show:102550
← PrevPage 22 of 197Next →

Benchmark Results

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