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 51–75 of 1963 papers

TitleStatusHype
Actually Sparse Variational Gaussian ProcessesCode1
Disentangling Derivatives, Uncertainty and Error in Gaussian Process ModelsCode1
Accounting for Input Noise in Gaussian Process Parameter RetrievalCode1
Diverse Text Generation via Variational Encoder-Decoder Models with Gaussian Process PriorsCode1
70 years of machine learning in geoscience in reviewCode1
PriorVAE: Encoding spatial priors with VAEs for small-area estimationCode1
Example-guided learning of stochastic human driving policies using deep reinforcement learningCode1
Exploration in Online Advertising Systems with Deep Uncertainty-Aware LearningCode1
Calibrating Transformers via Sparse Gaussian ProcessesCode1
GaPro: Box-Supervised 3D Point Cloud Instance Segmentation Using Gaussian Processes as Pseudo LabelersCode1
Gaussian processes at the Helm(holtz): A more fluid model for ocean currentsCode1
Bayes-Newton Methods for Approximate Bayesian Inference with PSD GuaranteesCode1
Variational multiple shooting for Bayesian ODEs with Gaussian processesCode1
BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decompositionCode1
Causal Discovery via Bayesian OptimizationCode1
Bayesian Active Learning with Fully Bayesian Gaussian ProcessesCode1
Batched Energy-Entropy acquisition for Bayesian OptimizationCode1
Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual InformationCode1
Time series forecasting with Gaussian Processes needs priorsCode1
Pre-trained Gaussian Processes for Bayesian OptimizationCode1
Bayesian Deep Ensembles via the Neural Tangent KernelCode1
Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian ProcessesCode1
Bayesian Optimization of Catalysis With In-Context LearningCode1
Bayesian Optimization of Function NetworksCode1
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