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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 61–70 of 1963 papers

TitleStatusHype
Bayesian Active Learning with Fully Bayesian Gaussian ProcessesCode1
Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual InformationCode1
Active Bayesian Causal InferenceCode1
Convolutional conditional neural processes for local climate downscalingCode1
Conformal Approach To Gaussian Process Surrogate Evaluation With Coverage GuaranteesCode1
Conditioning Sparse Variational Gaussian Processes for Online Decision-makingCode1
Constrained Causal Bayesian OptimizationCode1
Causal Discovery via Bayesian OptimizationCode1
Calibrating Transformers via Sparse Gaussian ProcessesCode1
Bayes-Newton Methods for Approximate Bayesian Inference with PSD GuaranteesCode1
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Benchmark Results

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