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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 501–525 of 1963 papers

TitleStatusHype
Physics-informed radial basis network (PIRBN): A local approximating neural network for solving nonlinear PDEsCode1
Primal-Dual Contextual Bayesian Optimization for Control System Online Optimization with Time-Average ConstraintsCode0
Actually Sparse Variational Gaussian ProcessesCode1
Cooperative Online Learning for Multi-Agent System Control via Gaussian Processes with Event-Triggered Mechanism: Extended Version—0
Bayesian Optimization of Catalysis With In-Context LearningCode1
PriorCVAE: scalable MCMC parameter inference with Bayesian deep generative modellingCode1
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
Applications of Gaussian Processes at Extreme Lengthscales: From Molecules to Black HolesCode1
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
Safe Machine-Learning-supported Model Predictive Force and Motion Control in Robotics—0
Model Predictive Control with Gaussian-Process-Supported Dynamical Constraints for Autonomous Vehicles—0
A switching Gaussian process latent force model for the identification of mechanical systems with a discontinuous nonlinearityCode0
Calibrating Transformers via Sparse Gaussian ProcessesCode1
Traffic State Estimation from Vehicle Trajectories with Anisotropic Gaussian ProcessesCode1
Learning Energy Conserving Dynamics Efficiently with Hamiltonian Gaussian ProcessesCode0
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Benchmark Results

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