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 501–510 of 1963 papers

TitleStatusHype
Deterministic Global Optimization of the Acquisition Function in Bayesian Optimization: To Do or Not To Do?—0
Dialogue manager domain adaptation using Gaussian process reinforcement learning—0
Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems—0
Arbitrarily-Conditioned Multi-Functional Diffusion for Multi-Physics Emulation—0
Differentially Private Gaussian Processes—0
Differentially Private Regression and Classification with Sparse Gaussian Processes—0
Differentiating the multipoint Expected Improvement for optimal batch design—0
Bayesian Kernel Shaping for Learning Control—0
Graph Based Gaussian Processes on Restricted Domains—0
A Framework for Finding Local Saddle Points in Two-Player Zero-Sum Black-Box Games—0
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Benchmark Results

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