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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 301–325 of 1963 papers

TitleStatusHype
Kriging and Gaussian Process Interpolation for Georeferenced Data Augmentation—0
Interpolation pour l'augmentation de donnees : Application à la gestion des adventices de la canne a sucre a la Reunion—0
Physics-informed Gaussian Processes for Safe Envelope ExpansionCode0
Multi-view Bayesian optimisation in reduced dimension for engineering design—0
Uncertainty-Aware Out-of-Distribution Detection with Gaussian Processes—0
Scalable Bayesian Optimization via Focalized Sparse Gaussian ProcessesCode0
Learning to Forget: Bayesian Time Series Forecasting using Recurrent Sparse Spectrum Signature Gaussian Processes—0
Bayesian Optimization of Bilevel Problems—0
Fast Multi-Group Gaussian Process Factor Models—0
Comparing noisy neural population dynamics using optimal transport distances—0
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
Data Efficient Prediction of excited-state properties using Quantum Neural Networks—0
Dimensionality Reduction Techniques for Global Bayesian Optimisation—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
Gaussian Processes for Probabilistic Estimates of Earthquake Ground Shaking: A 1-D Proof-of-ConceptCode0
FRIDAY: Real-time Learning DNN-based Stable LQR controller for Nonlinear Systems under Uncertain DisturbancesCode0
Physics-informed Gaussian Processes as Linear Model Predictive Controller—0
A Generalized Unified Skew-Normal Process with Neural Bayes Inference—0
Robust Bayesian Optimization via Localized Online Conformal PredictionCode0
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Benchmark Results

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