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 376–400 of 1963 papers

TitleStatusHype
A Gaussian Process Regression based Dynamical Models Learning Algorithm for Target Tracking—0
A temporal model of text periodicities using Gaussian Processes—0
Continuous surrogate-based optimization algorithms are well-suited for expensive discrete problems—0
Continuous-time edge modelling using non-parametric point processes—0
Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces—0
Control Barrier Functions for Unknown Nonlinear Systems using Gaussian Processes—0
Controllable Expensive Multi-objective Learning with Warm-starting Bayesian Optimization—0
A Fully-Automated Framework Integrating Gaussian Process Regression and Bayesian Optimization to Design Pin-Fins—0
Convergence and Concentration of Empirical Measures under Wasserstein Distance in Unbounded Functional Spaces—0
Convergence Guarantees for Gaussian Process Means With Misspecified Likelihoods and Smoothness—0
Convergence of Diffusion Models Under the Manifold Hypothesis in High-Dimensions—0
Comparative Analysis of Time Series Forecasting Approaches for Household Electricity Consumption Prediction—0
Deep Gaussian Processes for Biogeophysical Parameter Retrieval and Model Inversion—0
Deep Gaussian Processes for Few-Shot Segmentation—0
Compactly-supported nonstationary kernels for computing exact Gaussian processes on big data—0
Convolutional Normalizing Flows for Deep Gaussian Processes—0
Cooperative Learning with Gaussian Processes for Euler-Lagrange Systems Tracking Control under Switching Topologies—0
Correcting Model Bias with Sparse Implicit Processes—0
Correlated Product of Experts for Sparse Gaussian Process Regression—0
Correlational Gaussian Processes for Cross-Domain Visual Recognition—0
AUGUR, A flexible and efficient optimization algorithm for identification of optimal adsorption sites—0
A Receding Horizon Approach for Simultaneous Active Learning and Control using Gaussian Processes—0
DADEE: Well-calibrated uncertainty quantification in neural networks for barriers-based robot safety—0
DAG-GPs: Learning Directed Acyclic Graph Structure For Multi-Output Gaussian Processes—0
A brief note on understanding neural networks as Gaussian processes—0
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Benchmark Results

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