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 751–800 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
Safety-Aware Learning-Based Control of Systems with Uncertainty Dependent Constraints (extended version)—0
Active Learning for Regression with Aggregated Outputs—0
Log-Linear-Time Gaussian Processes Using Binary Tree KernelsCode0
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
Scalable Gaussian Process Hyperparameter Optimization via Coverage Regularization—0
Optimal Stopping with Gaussian Processes—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
TUM sebis at GermEval 2022: A Hybrid Model Leveraging Gaussian Processes and Fine-Tuned XLM-RoBERTa for German Text Complexity AnalysisCode0
Bézier Gaussian Processes for Tall and Wide Data—0
Data-Driven Chance Constrained AC-OPF using Hybrid Sparse Gaussian ProcessesCode0
Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations—0
Mixtures of Gaussian Process Experts with SMC^2—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
Bayesian Complementary Kernelized Learning for Multidimensional Spatiotemporal Data—0
Modelling spatio-temporal trends of air pollution in Africa—0
Visual Pursuit Control based on Gaussian Processes with Switched Motion TrajectoriesCode0
Quantum Bayesian Computation—0
Dynamic Bayesian Learning for Spatiotemporal Mechanistic ModelsCode0
Gaussian Process Surrogate Models for Neural Networks—0
Bayesian Optimization with Informative Covariance—0
Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs—0
Data-Driven Stochastic AC-OPF using Gaussian ProcessesCode0
Correcting Model Bias with Sparse Implicit Processes—0
Kullback-Leibler and Renyi divergences in reproducing kernel Hilbert space and Gaussian process settings—0
Learning inducing points and uncertainty on molecular data by scalable variational Gaussian processes—0
Comparative Analysis of Time Series Forecasting Approaches for Household Electricity Consumption Prediction—0
Infinite-Fidelity Coregionalization for Physical Simulation—0
Off-the-grid learning of mixtures from a continuous dictionary—0
On the Rényi Cross-Entropy—0
Distributional Gaussian Processes Layers for Out-of-Distribution Detection—0
Aggregated Multi-output Gaussian Processes with Knowledge Transfer Across Domains—0
Physically Consistent Learning of Conservative Lagrangian Systems with Gaussian Processes—0
A generalised form for a homogeneous population of structures using an overlapping mixture of Gaussian processes—0
Sparse Kernel Gaussian Processes through Iterative Charted Refinement (ICR)—0
Additive Gaussian Processes RevisitedCode0
Shallow and Deep Nonparametric Convolutions for Gaussian ProcessesCode0
On Integrating Prior Knowledge into Gaussian Processes for Prognostic Health Monitoring—0
Learning Physics between Digital Twins with Low-Fidelity Models and Physics-Informed Gaussian ProcessesCode0
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Benchmark Results

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