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 271–280 of 1963 papers

TitleStatusHype
Partially Observable Gaussian Process Network and Doubly Stochastic Variational Inference—0
Pushing the Limits of the Reactive Affine Shaker Algorithm to Higher Dimensions—0
Learning Surrogate Potential Mean Field Games via Gaussian Processes: A Data-Driven Approach to Ill-Posed Inverse Problems—0
Locally-Deployed Chain-of-Thought (CoT) Reasoning Model in Chemical Engineering: Starting from 30 Experimental Data—0
From Deep Additive Kernel Learning to Last-Layer Bayesian Neural Networks via Induced Prior ApproximationCode0
Recurrent Memory for Online Interdomain Gaussian Processes—0
New Bounds for Sparse Variational Gaussian Processes—0
Epistemic Uncertainty in Conformal Scores: A Unified ApproachCode0
Koopman-Equivariant Gaussian Processes—0
Bayesian Optimization by Kernel Regression and Density-based Exploration—0
Show:102550
← PrevPage 28 of 197Next →

Benchmark Results

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