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 1726–1750 of 1963 papers

TitleStatusHype
Faster Kernel Interpolation for Gaussian Processes—0
Faster variational inducing input Gaussian process classification—0
Fast Gaussian Processes under Monotonicity Constraints—0
Fast Gaussian Process Posterior Mean Prediction via Local Cross Validation and Precomputation—0
Fast Gaussian Process Regression for Big Data—0
Fast Inverter Control by Learning the OPF Mapping using Sensitivity-Informed Gaussian Processes—0
Fast Kernel Learning for Multidimensional Pattern Extrapolation—0
Fast methods for training Gaussian processes on large data sets—0
Fast Multi-Group Gaussian Process Factor Models—0
Forward variable selection enables fast and accurate dynamic system identification with Karhunen-Loève decomposed Gaussian processes—0
Federated Automatic Latent Variable Selection in Multi-output Gaussian Processes—0
Few-shot Learning for Spatial Regression—0
Financial Applications of Gaussian Processes and Bayesian Optimization—0
Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection—0
Finite sample approximations of exact and entropic Wasserstein distances between covariance operators and Gaussian processes—0
Finite size corrections for neural network Gaussian processes—0
Flow Matching with Gaussian Process Priors for Probabilistic Time Series Forecasting—0
Forecasting intermittent time series with Gaussian Processes and Tweedie likelihood—0
Forecasting of commercial sales with large scale Gaussian Processes—0
Forecasting Wireless Demand with Extreme Values using Feature Embedding in Gaussian Processes—0
Fractional Barndorff-Nielsen and Shephard model: applications in variance and volatility swaps, and hedging—0
Frequency Domain Gaussian Process Models for H^ Uncertainties—0
Frequency-domain Gaussian Process Models for H_ Uncertainties—0
From Prediction to Action: Critical Role of Performance Estimation for Machine-Learning-Driven Materials Discovery—0
Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes—0
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Benchmark Results

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