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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 311–320 of 1963 papers

TitleStatusHype
Regional Expected Improvement for Efficient Trust Region Selection in High-Dimensional Bayesian OptimizationCode0
Task Diversity in Bayesian Federated Learning: Simultaneous Processing of Classification and RegressionCode0
Adaptive Sampling to Reduce Epistemic Uncertainty Using Prediction Interval-Generation Neural NetworksCode0
Dimensionality Reduction Techniques for Global Bayesian Optimisation—0
Data Efficient Prediction of excited-state properties using Quantum Neural Networks—0
Bayesian Optimization via Continual Variational Last Layer Training—0
Epidemiological Model Calibration via Graybox Bayesian Optimization—0
Nonmyopic Global Optimisation via Approximate Dynamic ProgrammingCode0
Uncertainty Quantification for Transformer Models for Dark-Pattern Detection—0
Fixed-Mean Gaussian Processes for Post-hoc Bayesian Deep LearningCode0
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Benchmark Results

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