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 751–775 of 1963 papers

TitleStatusHype
Inferring Smooth Control: Monte Carlo Posterior Policy Iteration with Gaussian ProcessesCode0
Understanding Neural Coding on Latent Manifolds by Sharing Features and Dividing EnsemblesCode0
Inference on Causal Effects of Interventions in Time using Gaussian Processes—0
Log-Linear-Time Gaussian Processes Using Binary Tree KernelsCode0
Active Learning for Regression with Aggregated Outputs—0
Safety-Aware Learning-Based Control of Systems with Uncertainty Dependent Constraints (extended version)—0
Temporal Knowledge Graph Completion with Approximated Gaussian Process Embedding—0
Physically Meaningful Uncertainty Quantification in Probabilistic Wind Turbine Power Curve Models as a Damage Sensitive Feature—0
Optimal Stopping with Gaussian Processes—0
Scalable Gaussian Process Hyperparameter Optimization via Coverage Regularization—0
Partial sequence labeling with structured Gaussian Processes—0
Interrelation of equivariant Gaussian processes and convolutional neural networks—0
Kernel Learning for Explainable Climate ScienceCode0
Revisiting Active Sets for Gaussian Process DecodersCode0
Causal Modeling of Policy Interventions From Sequences of Treatments and Outcomes—0
Optimal Sensor Placement in Body Surface Networks using Gaussian Processes—0
Active learning-assisted neutron spectroscopy with log-Gaussian processes—0
Bézier Gaussian Processes for Tall and Wide Data—0
TUM sebis at GermEval 2022: A Hybrid Model Leveraging Gaussian Processes and Fine-Tuned XLM-RoBERTa for German Text Complexity AnalysisCode0
Data-Driven Chance Constrained AC-OPF using Hybrid Sparse Gaussian ProcessesCode0
Mixtures of Gaussian Process Experts with SMC^2—0
Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations—0
Fast emulation of density functional theory simulations using approximate Gaussian processes—0
Learning linear modules in a dynamic network with missing node observations—0
Scale invariant process regression: Towards Bayesian ML with minimal assumptions—0
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Benchmark Results

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