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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 1176–1200 of 1963 papers

TitleStatusHype
Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation—0
Beyond Grids: Multi-objective Bayesian Optimization With Adaptive DiscretizationCode0
Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian ProcessesCode1
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian OptimizationCode0
Likelihood-Free Inference with Deep Gaussian ProcessesCode0
Infinite attention: NNGP and NTK for deep attention networks—0
Towards Recurrent Autoregressive Flow Models—0
Matérn Gaussian processes on Riemannian manifoldsCode1
70 years of machine learning in geoscience in reviewCode1
Real-Time Regression with Dividing Local Gaussian Processes—0
Safety Verification of Unknown Dynamical Systems via Gaussian Process Regression—0
Lateral land movement prediction from GNSS position time series in a machine learning aided algorithm—0
GP3: A Sampling-based Analysis Framework for Gaussian Processes—0
Gaussian Processes on Graphs via Spectral Kernel Learning—0
Uncertainty quantification using martingales for misspecified Gaussian processesCode0
Fast Deep Mixtures of Gaussian Process Experts—0
Scalable Partial Explainability in Neural Networks via Flexible Activation Functions—0
Syn2Real Transfer Learning for Image Deraining using Gaussian ProcessesCode1
Variational Auto-Regressive Gaussian Processes for Continual LearningCode1
Smart Forgetting for Safe Online Learning with Gaussian Processes—0
Learning supported Model Predictive Control for Tracking of Periodic References—0
Regret Bound for Safe Gaussian Process Bandit Optimization—0
Learning Constrained Dynamics with Gauss' Principle adhering Gaussian ProcessesCode0
Multi-Fidelity High-Order Gaussian Processes for Physical SimulationCode0
Physics Informed Deep Kernel Learning—0
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Benchmark Results

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