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 1926–1950 of 1963 papers

TitleStatusHype
The Ridgelet Prior: A Covariance Function Approach to Prior Specification for Bayesian Neural NetworksCode0
Learning Integral Representations of Gaussian ProcessesCode0
Learning in the Wild with Incremental Skeptical Gaussian ProcessesCode0
Bayesian Learning from Sequential Data using Gaussian Processes with Signature CovariancesCode0
Parametric Gaussian Process Regression for Big DataCode0
Beyond Grids: Multi-objective Bayesian Optimization With Adaptive DiscretizationCode0
A Bayesian Perspective of Statistical Machine Learning for Big DataCode0
Partial Separability and Functional Graphical Models for Multivariate Gaussian ProcessesCode0
Amortized Variational Inference: When and Why?Code0
Combining Pseudo-Point and State Space Approximations for Sum-Separable Gaussian ProcessesCode0
Learning Nonparametric Volterra Kernels with Gaussian ProcessesCode0
Learning ODE Models with Qualitative Structure Using Gaussian ProcessesCode0
Scalable Generalized Dynamic Topic ModelsCode0
Learning of Weighted Multi-layer Networks via Dynamic Social Spaces, with Application to Financial Interbank TransactionsCode0
Learning Physics between Digital Twins with Low-Fidelity Models and Physics-Informed Gaussian ProcessesCode0
PDE-DKL: PDE-constrained deep kernel learning in high dimensionalityCode0
Scalable high-resolution forecasting of sparse spatiotemporal events with kernel methods: a winning solution to the NIJ "Real-Time Crime Forecasting Challenge"Code0
Deep Gaussian Processes for Multi-fidelity ModelingCode0
Learning Scalable Deep Kernels with Recurrent StructureCode0
Scalable Hyperparameter Optimization with Products of Gaussian Process ExpertsCode0
Scalable Hyperparameter Optimization with Lazy Gaussian ProcessesCode0
Cluster-Specific Predictions with Multi-Task Gaussian ProcessesCode0
Personalized Gaussian Processes for Forecasting of Alzheimer's Disease Assessment Scale-Cognition Sub-Scale (ADAS-Cog13)Code0
Learning Stochastic Differential Equations With Gaussian Processes Without Gradient MatchingCode0
Personalized Gaussian Processes for Future Prediction of Alzheimer's Disease ProgressionCode0
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Benchmark Results

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