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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 351–375 of 1963 papers

TitleStatusHype
Complex-Valued Gaussian Processes for Regression—0
Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference—0
A Sparse Gaussian Process Framework for Photometric Redshift Estimation—0
Conditional Generative Modeling for Images, 3D Animations, and Video—0
Conditionally-Conjugate Gaussian Process Factor Analysis for Spike Count Data via Data Augmentation—0
A spectrum of physics-informed Gaussian processes for regression in engineering—0
Comparing noisy neural population dynamics using optimal transport distances—0
Conditional Neural Processes for Molecules—0
Conditioning of Banach Space Valued Gaussian Random Variables: An Approximation Approach Based on Martingales—0
A Robust Asymmetric Kernel Function for Bayesian Optimization, with Application to Image Defect Detection in Manufacturing Systems—0
Confirmatory Bayesian Online Change Point Detection in the Covariance Structure of Gaussian Processes—0
A Fully-Automated Framework Integrating Gaussian Process Regression and Bayesian Optimization to Design Pin-Fins—0
Conformal Prediction for Manifold-based Source Localization with Gaussian Processes—0
Connections and Equivalences between the Nyström Method and Sparse Variational Gaussian Processes—0
A Statistical Machine Learning Approach to Yield Curve Forecasting—0
Consistency of some sequential experimental design strategies for excursion set estimation based on vector-valued Gaussian processes—0
Consistent Online Gaussian Process Regression Without the Sample Complexity Bottleneck—0
Optimal Privacy-Aware Stochastic Sampling—0
Constrained Bayesian Optimization under Bivariate Gaussian Process with Application to Cure Process Optimization—0
Comparative Analysis of Time Series Forecasting Approaches for Household Electricity Consumption Prediction—0
Constraining Gaussian processes for physics-informed acoustic emission mapping—0
Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations—0
Constructing Gaussian Processes via Samplets—0
Contextual Combinatorial Multi-output GP Bandits with Group Constraints—0
Data Efficient Prediction of excited-state properties using Quantum Neural Networks—0
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Benchmark Results

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