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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 101–125 of 1963 papers

TitleStatusHype
Positional Encoder Graph Neural Networks for Geographic DataCode1
Bayes-Newton Methods for Approximate Bayesian Inference with PSD GuaranteesCode1
Spatio-Temporal Variational Gaussian ProcessesCode1
Conditioning Sparse Variational Gaussian Processes for Online Decision-makingCode1
Modular Gaussian Processes for Transfer LearningCode1
Non-Gaussian Gaussian Processes for Few-Shot RegressionCode1
PriorVAE: Encoding spatial priors with VAEs for small-area estimationCode1
Nonnegative spatial factorizationCode1
Learning to Pick at Non-Zero-Velocity from Interactive DemonstrationsCode1
Dense Gaussian Processes for Few-Shot SegmentationCode1
Pre-trained Gaussian Processes for Bayesian OptimizationCode1
Deep Gaussian Process Emulation using Stochastic ImputationCode1
Personalized Federated Learning with Gaussian ProcessesCode1
Variational multiple shooting for Bayesian ODEs with Gaussian processesCode1
Transfer Bayesian Meta-learning via Weighted Free Energy MinimizationCode1
SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian ProcessesCode1
Scalable Variational Gaussian Processes via Harmonic Kernel DecompositionCode1
Federated Estimation of Causal Effects from Observational DataCode1
GPy-ABCD: A Configurable Automatic Bayesian Covariance Discovery ImplementationCode1
Relative Positional Encoding for Transformers with Linear ComplexityCode1
MuyGPs: Scalable Gaussian Process Hyperparameter Estimation Using Local Cross-ValidationCode1
Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional StructureCode1
Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual InformationCode1
GPflux: A Library for Deep Gaussian ProcessesCode1
Solving and Learning Nonlinear PDEs with Gaussian ProcessesCode1
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Benchmark Results

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