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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 476–500 of 1963 papers

TitleStatusHype
Density Ratio Estimation-based Bayesian Optimization with Semi-Supervised Learning—0
Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models—0
Deep Pipeline Embeddings for AutoMLCode1
SMT 2.0: A Surrogate Modeling Toolbox with a focus on Hierarchical and Mixed Variables Gaussian ProcessesCode2
MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under non-parameterized geometrical variabilityCode0
Physics Inspired Approaches To Understanding Gaussian ProcessesCode1
Posterior Inference on Shallow Infinitely Wide Bayesian Neural Networks under Weights with Unbounded VarianceCode0
Dynamic Term Structure Models with Nonlinearities using Gaussian Processes—0
Quantum neural networks form Gaussian processes—0
Learning Switching Port-Hamiltonian Systems with Uncertainty Quantification—0
Meta-models for transfer learning in source localisation—0
NUBO: A Transparent Python Package for Bayesian OptimizationCode1
Disentangled Multi-Fidelity Deep Bayesian Active LearningCode1
Optimizing Hyperparameters with Conformal Quantile RegressionCode1
Representing Additive Gaussian Processes by Sparse Matrices—0
A Chain Rule for the Expected Suprema of Bernoulli Processes—0
Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes—0
Mixtures of Gaussian process experts based on kernel stick-breaking processesCode0
Quantum Gaussian Process Regression for Bayesian Optimization—0
Stochastic MPC for energy hubs using data driven demand forecasting—0
Self-Correcting Bayesian Optimization through Bayesian Active Learning—0
Martingale Posterior Neural Processes—0
Neural networks for geospatial data—0
Promises and Pitfalls of the Linearized Laplace in Bayesian OptimizationCode0
Dimensionality Reduction as Probabilistic Inference—0
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Benchmark Results

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