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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 1071–1080 of 1963 papers

TitleStatusHype
Output-Weighted Sampling for Multi-Armed Bandits with Extreme PayoffsCode0
Non-asymptotic approximations of neural networks by Gaussian processes—0
Using Distance Correlation for Efficient Bayesian Optimization—0
Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients—0
Double-descent curves in neural networks: a new perspective using Gaussian processes—0
Bias-Free Scalable Gaussian Processes via Randomized TruncationsCode0
Uncertainty-Aware Semi-Supervised Method Using Large Unlabeled and Limited Labeled COVID-19 Data—0
Attentive Gaussian processes for probabilistic time-series generation—0
Latent Map Gaussian Processes for Mixed Variable Metamodeling—0
Using Gaussian Processes to Design Dynamic Experiments for Black-Box Model Discrimination under Uncertainty—0
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Benchmark Results

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