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 126–150 of 1963 papers

TitleStatusHype
Random Forests for dependent dataCode1
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
A Unifying Variational Framework for Gaussian Process Motion PlanningCode1
Time series forecasting with Gaussian Processes needs priorsCode1
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
An Intuitive Tutorial to Gaussian Process RegressionCode1
Bayesian Optimization of Function NetworksCode1
BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decompositionCode1
Building 3D Morphable Models from a Single ScanCode1
A tutorial on learning from preferences and choices with Gaussian ProcessesCode1
A Rate-Distortion View of Uncertainty QuantificationCode1
Conditioning Sparse Variational Gaussian Processes for Online Decision-makingCode1
Conformal Approach To Gaussian Process Surrogate Evaluation With Coverage GuaranteesCode1
Data-Driven Autoencoder Numerical Solver with Uncertainty Quantification for Fast Physical SimulationsCode1
Deep Gaussian Process-based Multi-fidelity Bayesian Optimization for Simulated Chemical ReactorsCode1
Applications of Gaussian Processes at Extreme Lengthscales: From Molecules to Black HolesCode1
Deep Kernel LearningCode1
Deep Pipeline Embeddings for AutoMLCode1
Deep Random Features for Scalable Interpolation of Spatiotemporal DataCode1
Deep State-Space Gaussian ProcessesCode1
A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with Skew Gaussian ProcessesCode1
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Benchmark Results

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