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 651–675 of 1963 papers

TitleStatusHype
Cooperative Online Learning for Multi-Agent System Control via Gaussian Processes with Event-Triggered Mechanism: Extended Version—0
Wide neural networks: From non-gaussian random fields at initialization to the NTK geometry of training—0
Beyond Unimodal: Generalising Neural Processes for Multimodal Uncertainty Estimation—0
Sparse Cholesky Factorization for Solving Nonlinear PDEs via Gaussian ProcessesCode0
Neural signature kernels as infinite-width-depth-limits of controlled ResNetsCode0
Sparse Gaussian Processes with Spherical Harmonic Features Revisited—0
GP-PCS: One-shot Feature-Preserving Point Cloud Simplification with Gaussian Processes on Riemannian ManifoldsCode0
Stochastic Model Predictive Control Utilizing Bayesian Neural Networks—0
Clustering based on Mixtures of Sparse Gaussian Processes—0
Chance Constrained Stochastic Optimal Control for Arbitrarily Disturbed LTI Systems Via the One-Sided Vysochanskij-Petunin Inequality—0
Hierarchical-Hyperplane Kernels for Actively Learning Gaussian Process Models of Nonstationary SystemsCode0
Gaussian Process on the Product of Directional Manifolds—0
Reconstructing the Hubble parameter with future Gravitational Wave missions using Machine Learning—0
Model Predictive Control with Gaussian-Process-Supported Dynamical Constraints for Autonomous Vehicles—0
Safe Machine-Learning-supported Model Predictive Force and Motion Control in Robotics—0
A switching Gaussian process latent force model for the identification of mechanical systems with a discontinuous nonlinearityCode0
Learning-based Position and Stiffness Feedforward Control of Antagonistic Soft Pneumatic Actuators using Gaussian Processes—0
Learning Energy Conserving Dynamics Efficiently with Hamiltonian Gaussian ProcessesCode0
Bayesian Kernelized Tensor Factorization as Surrogate for Bayesian Optimization—0
Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces—0
Random forests for binary geospatial data—0
Sharp Calibrated Gaussian Processes—0
Improved uncertainty quantification for neural networks with Bayesian last layerCode0
Non-separable Covariance Kernels for Spatiotemporal Gaussian Processes based on a Hybrid Spectral Method and the Harmonic Oscillator—0
A Meta-Learning Approach to Population-Based Modelling of Structures—0
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Benchmark Results

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