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 321–330 of 1963 papers

TitleStatusHype
Voronoi Candidates for Bayesian OptimizationCode0
Combining additivity and active subspaces for high-dimensional Gaussian process modeling—0
Standard Gaussian Process Can Be Excellent for High-Dimensional Bayesian OptimizationCode1
Decentralized Event-Triggered Online Learning for Safe Consensus of Multi-Agent Systems with Gaussian Process Regression—0
Cooperative Learning with Gaussian Processes for Euler-Lagrange Systems Tracking Control under Switching Topologies—0
Co-orchestration of Multiple Instruments to Uncover Structure-Property Relationships in Combinatorial LibrariesCode0
Neural variational Data Assimilation with Uncertainty Quantification using SPDE priors—0
Self-Attention through Kernel-Eigen Pair Sparse Variational Gaussian ProcessesCode1
Quantum-Assisted Hilbert-Space Gaussian Process RegressionCode0
Bayesian Causal Inference with Gaussian Process NetworksCode0
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Benchmark Results

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