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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1621–1630 of 1963 papers

TitleStatusHype
Design of Experiments for Verifying Biomolecular Networks—0
Detecting British Columbia Coastal Rainfall Patterns by Clustering Gaussian Processes—0
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
Diffusion-BBO: Diffusion-Based Inverse Modeling for Online Black-Box Optimization—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
Graph Based Gaussian Processes on Restricted Domains—0
Diffusion models for Gaussian distributions: Exact solutions and Wasserstein errors—0
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Benchmark Results

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