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 581–590 of 1963 papers

TitleStatusHype
Benefits of Monotonicity in Safe Exploration with Gaussian ProcessesCode0
Heterogeneous Multi-Task Gaussian Cox ProcessesCode0
Finding Non-Uniform Quantization Schemes using Multi-Task Gaussian ProcessesCode0
Federated Learning for Non-factorizable Models using Deep Generative Prior ApproximationsCode0
Federated Causal Inference from Observational DataCode0
Beyond Intuition, a Framework for Applying GPs to Real-World DataCode0
Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive UncertaintiesCode0
Fixed-Mean Gaussian Processes for Post-hoc Bayesian Deep LearningCode0
How Bayesian Should Bayesian Optimisation Be?Code0
Fast Evaluation of Additive Kernels: Feature Arrangement, Fourier Methods, and Kernel DerivativesCode0
Show:102550
← PrevPage 59 of 197Next →

Benchmark Results

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