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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 26–50 of 1963 papers

TitleStatusHype
Accounting for Input Noise in Gaussian Process Parameter RetrievalCode1
70 years of machine learning in geoscience in reviewCode1
Applications of Gaussian Processes at Extreme Lengthscales: From Molecules to Black HolesCode1
DeepKriging: Spatially Dependent Deep Neural Networks for Spatial PredictionCode1
A Rate-Distortion View of Uncertainty QuantificationCode1
Deep Reinforcement Learning for Human-Like Driving Policies in Collision Avoidance Tasks of Self-Driving CarsCode1
Causal Discovery via Bayesian OptimizationCode1
Calibrating Transformers via Sparse Gaussian ProcessesCode1
Conditional Neural ProcessesCode1
Bayes-Newton Methods for Approximate Bayesian Inference with PSD GuaranteesCode1
A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics IdentificationCode1
Variational multiple shooting for Bayesian ODEs with Gaussian processesCode1
BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decompositionCode1
Conditioning Sparse Variational Gaussian Processes for Online Decision-makingCode1
Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual InformationCode1
Bayesian Optimization of Catalysis With In-Context LearningCode1
Bayesian Optimization of Function NetworksCode1
Active Bayesian Causal InferenceCode1
Building 3D Morphable Models from a Single ScanCode1
Bayesian Deep Ensembles via the Neural Tangent KernelCode1
Batched Energy-Entropy acquisition for Bayesian OptimizationCode1
Constrained Causal Bayesian OptimizationCode1
Convergence of Sparse Variational Inference in Gaussian Processes RegressionCode1
Pre-trained Gaussian Processes for Bayesian OptimizationCode1
Bayesian Active Learning with Fully Bayesian Gaussian ProcessesCode1
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Benchmark Results

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