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 481–490 of 1963 papers

TitleStatusHype
Gradient-enhanced deep Gaussian processes for multifidelity modelling—0
Enhancing Mean-Reverting Time Series Prediction with Gaussian Processes: Functional and Augmented Data Structures in Financial Forecasting—0
Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive OrdersCode0
Global Safe Sequential Learning via Efficient Knowledge TransferCode0
Motion Code: Robust Time Series Classification and Forecasting via Sparse Variational Multi-Stochastic Processes LearningCode0
Data-Driven Stochastic AC-OPF using Gaussian ProcessesCode0
Resilience of Rademacher chaos of low degree—0
Nowcasting with Mixed Frequency Data Using Gaussian Processes—0
Recommendations for Baselines and Benchmarking Approximate Gaussian Processes—0
Neural Networks Asymptotic Behaviours for the Resolution of Inverse Problems—0
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Benchmark Results

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