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 1551–1575 of 1963 papers

TitleStatusHype
Control Barrier Functions for Unknown Nonlinear Systems using Gaussian Processes—0
Controllable Expensive Multi-objective Learning with Warm-starting Bayesian Optimization—0
Controller Adaptation via Learning Solutions of Contextual Bayesian Optimization—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
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
Current Methods for Drug Property Prediction in the Real World—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
Daily Land Surface Temperature Reconstruction in Landsat Cross-Track Areas Using Deep Ensemble Learning With Uncertainty Quantification—0
Damage detection in operational wind turbine blades using a new approach based on machine learning—0
Data Association with Gaussian Processes—0
Data-Driven Approaches for Modelling Target Behaviour—0
Data-driven Bayesian Control of Port-Hamiltonian Systems—0
Learning particle swarming models from data with Gaussian processes—0
Data-driven Force Observer for Human-Robot Interaction with Series Elastic Actuators using Gaussian Processes—0
Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long Short-Term Memory Networks—0
Data-driven identification of port-Hamiltonian DAE systems by Gaussian processes—0
Data-Driven Model Selections of Second-Order Particle Dynamics via Integrating Gaussian Processes with Low-Dimensional Interacting Structures—0
Data-driven Output Regulation via Gaussian Processes and Luenberger Internal Models—0
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Benchmark Results

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