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 71–80 of 1963 papers

TitleStatusHype
Forecasting intermittent time series with Gaussian Processes and Tweedie likelihood—0
Daily Land Surface Temperature Reconstruction in Landsat Cross-Track Areas Using Deep Ensemble Learning With Uncertainty Quantification—0
Provable Quantum Algorithm Advantage for Gaussian Process QuadratureCode0
Robust Optimization with Diffusion Models for Green Security—0
Experiment Design with Gaussian Process Regression with Applications to Chance-Constrained Control—0
Partially Observable Gaussian Process Network and Doubly Stochastic Variational Inference—0
Pushing the Limits of the Reactive Affine Shaker Algorithm to Higher Dimensions—0
Locally-Deployed Chain-of-Thought (CoT) Reasoning Model in Chemical Engineering: Starting from 30 Experimental Data—0
Learning Surrogate Potential Mean Field Games via Gaussian Processes: A Data-Driven Approach to Ill-Posed Inverse Problems—0
From Deep Additive Kernel Learning to Last-Layer Bayesian Neural Networks via Induced Prior ApproximationCode0
Show:102550
← PrevPage 8 of 197Next →

Benchmark Results

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