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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 676–700 of 1963 papers

TitleStatusHype
A Receding Horizon Approach for Simultaneous Active Learning and Control using Gaussian Processes—0
Financial Applications of Gaussian Processes and Bayesian Optimization—0
Compactly-supported nonstationary kernels for computing exact Gaussian processes on big data—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
A Fully-Automated Framework Integrating Gaussian Process Regression and Bayesian Optimization to Design Pin-Fins—0
Comparing noisy neural population dynamics using optimal transport distances—0
A Robust Asymmetric Kernel Function for Bayesian Optimization, with Application to Image Defect Detection in Manufacturing Systems—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
Composite likelihood estimation of stationary Gaussian processes with a view toward stochastic volatility—0
Compositionally-Warped Gaussian Processes—0
From Prediction to Action: Critical Role of Performance Estimation for Machine-Learning-Driven Materials Discovery—0
A Perspective on Gaussian Processes for Earth Observation—0
Fully Bayesian Differential Gaussian Processes through Stochastic Differential Equations—0
Data-Driven Abstractions via Binary-Tree Gaussian Processes for Formal Verification—0
Fully Decentralized, Scalable Gaussian Processes for Multi-Agent Federated Learning—0
Fully Scalable Gaussian Processes using Subspace Inducing Inputs—0
Bayesian Variational Optimization for Combinatorial Spaces—0
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Benchmark Results

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