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 76–100 of 1963 papers

TitleStatusHype
A Rate-Distortion View of Uncertainty QuantificationCode1
Disentangling Derivatives, Uncertainty and Error in Gaussian Process ModelsCode1
Deep Kernel LearningCode1
A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics IdentificationCode1
A tutorial on learning from preferences and choices with Gaussian ProcessesCode1
A Unifying Variational Framework for Gaussian Process Motion PlanningCode1
Time series forecasting with Gaussian Processes needs priorsCode1
Convolutional conditional neural processes for local climate downscalingCode1
AutoIP: A United Framework to Integrate Physics into Gaussian ProcessesCode1
Exact, Fast and Expressive Poisson Point Processes via Squared Neural FamiliesCode1
Example-guided learning of stochastic human driving policies using deep reinforcement learningCode1
Pre-trained Gaussian Processes for Bayesian OptimizationCode1
Bayesian Active Learning with Fully Bayesian Gaussian ProcessesCode1
Data-Driven Autoencoder Numerical Solver with Uncertainty Quantification for Fast Physical SimulationsCode1
GaPro: Box-Supervised 3D Point Cloud Instance Segmentation Using Gaussian Processes as Pseudo LabelersCode1
Bayesian Meta-Learning for the Few-Shot Setting via Deep KernelsCode1
Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual InformationCode1
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
Conformal Approach To Gaussian Process Surrogate Evaluation With Coverage GuaranteesCode1
GP-BART: a novel Bayesian additive regression trees approach using Gaussian processesCode1
Conditioning Sparse Variational Gaussian Processes for Online Decision-makingCode1
Bayesian Optimization of Function NetworksCode1
Constrained Causal Bayesian OptimizationCode1
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Benchmark Results

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