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 351–375 of 1963 papers

TitleStatusHype
Flow Matching with Gaussian Process Priors for Probabilistic Time Series Forecasting—0
Deep Kernel Posterior Learning under Infinite Variance Prior WeightsCode0
GPTreeO: An R package for continual regression with dividing local Gaussian processes—0
Stream-level flow matching with Gaussian processesCode0
Convergence of Diffusion Models Under the Manifold Hypothesis in High-Dimensions—0
Safe Time-Varying Optimization based on Gaussian Processes with Spatio-Temporal Kernel—0
Reactive Multi-Robot Navigation in Outdoor Environments Through Uncertainty-Aware Active Learning of Human Preference Landscape—0
AUGUR, A flexible and efficient optimization algorithm for identification of optimal adsorption sites—0
Physics-Informed Variational State-Space Gaussian ProcessesCode0
Theoretical Analysis of Heteroscedastic Gaussian Processes with Posterior Distributions—0
Amortized Variational Inference for Deep Gaussian Processes—0
Conformal Prediction for Manifold-based Source Localization with Gaussian Processes—0
Decomposing Gaussians with Unknown CovarianceCode0
Probabilistic Spatiotemporal Modeling of Day-Ahead Wind Power Generation with Input-Warped Gaussian Processes—0
Predicting Electricity Consumption with Random Walks on Gaussian Processes—0
Inference for Large Scale Regression Models with Dependent Errors—0
Heartbeat classification using various machine learning models: A comparative studyCode0
Multi-Task Combinatorial Bandits for Budget Allocation—0
Safe Bayesian Optimization for Complex Control Systems via Additive Gaussian Processes—0
Bayesian optimization of atomic structures with prior probabilities from universal interatomic potentialsCode0
Turbine location-aware multi-decadal wind power predictions for Germany using CMIP6—0
A Unified Theory of Quantum Neural Network Loss Landscapes—0
Active Learning of Molecular Data for Task-Specific ObjectivesCode0
Gaussian Processes with Noisy Regression Inputs for Dynamical Systems—0
Incremental Structure Discovery of Classification via Sequential Monte Carlo—0
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Benchmark Results

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