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 691–700 of 1963 papers

TitleStatusHype
Deterministic Global Optimization of the Acquisition Function in Bayesian Optimization: To Do or Not To Do?—0
Bayesian Inference of Log Determinants—0
Analysis of Nonstationary Time Series Using Locally Coupled Gaussian Processes—0
Detecting British Columbia Coastal Rainfall Patterns by Clustering Gaussian Processes—0
Design of Experiments for Verifying Biomolecular Networks—0
Bayesian Inference in High-Dimensional Time-Serieswith the Orthogonal Stochastic Linear Mixing Model—0
Using Gaussian Processes to Design Dynamic Experiments for Black-Box Model Discrimination under Uncertainty—0
Designing Robust Biotechnological Processes Regarding Variabilities using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design—0
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC—0
Analysis of Financial Credit Risk Using Machine Learning—0
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Benchmark Results

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