SOTAVerified

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 26–50 of 1963 papers

TitleStatusHype
Accounting for Input Noise in Gaussian Process Parameter RetrievalCode1
70 years of machine learning in geoscience in reviewCode1
Deep Gaussian Process Emulation using Stochastic ImputationCode1
Bayesian Meta-Learning for the Few-Shot Setting via Deep KernelsCode1
DeepKriging: Spatially Dependent Deep Neural Networks for Spatial PredictionCode1
Calibrating Transformers via Sparse Gaussian ProcessesCode1
Bayes-Newton Methods for Approximate Bayesian Inference with PSD GuaranteesCode1
Causal Discovery via Bayesian OptimizationCode1
Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian ProcessesCode1
A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics IdentificationCode1
Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual InformationCode1
Variational multiple shooting for Bayesian ODEs with Gaussian processesCode1
AutoIP: A United Framework to Integrate Physics into Gaussian ProcessesCode1
Active Bayesian Causal InferenceCode1
Bayesian Active Learning with Fully Bayesian Gaussian ProcessesCode1
Bayesian Deep Ensembles via the Neural Tangent KernelCode1
Bayesian Deep Learning and a Probabilistic Perspective of GeneralizationCode1
Bayesian Optimization of Catalysis With In-Context LearningCode1
Bayesian Optimization of Function NetworksCode1
BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decompositionCode1
Building 3D Morphable Models from a Single ScanCode1
Conditional Neural ProcessesCode1
Conditioning Sparse Variational Gaussian Processes for Online Decision-makingCode1
Constrained Causal Bayesian OptimizationCode1
Time series forecasting with Gaussian Processes needs priorsCode1
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Benchmark Results

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