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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 1–50 of 1963 papers

TitleStatusHype
Chronos: Learning the Language of Time SeriesCode7
The GeometricKernels Package: Heat and Matérn Kernels for Geometric Learning on Manifolds, Meshes, and GraphsCode4
Adversarial Robustness Toolbox v1.0.0Code3
MathOptAI.jl: Embed trained machine learning predictors into JuMP modelsCode2
Statistical Machine Learning for Astronomy -- A TextbookCode2
Real-time Spatial-temporal Traversability Assessment via Feature-based Sparse Gaussian ProcessCode2
L4acados: Learning-based models for acados, applied to Gaussian process-based predictive controlCode2
High-dimensional mixed-categorical Gaussian processes with application to multidisciplinary design optimization for a green aircraftCode2
CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear AlgebraCode2
SMT 2.0: A Surrogate Modeling Toolbox with a focus on Hierarchical and Mixed Variables Gaussian ProcessesCode2
Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlowCode2
GAUCHE: A Library for Gaussian Processes in ChemistryCode2
A Framework for Interdomain and Multioutput Gaussian ProcessesCode2
Convolutional Gaussian ProcessesCode2
GPflow: A Gaussian process library using TensorFlowCode2
Gaussian Processes for Big DataCode2
LLINBO: Trustworthy LLM-in-the-Loop Bayesian OptimizationCode1
GP-GS: Gaussian Processes for Enhanced Gaussian SplattingCode1
Causal Discovery via Bayesian OptimizationCode1
Improving Pareto Set Learning for Expensive Multi-objective Optimization via Stein Variational HypernetworksCode1
Deep Random Features for Scalable Interpolation of Spatiotemporal DataCode1
MetaMetrics-MT: Tuning Meta-Metrics for Machine Translation via Human Preference CalibrationCode1
Batched Energy-Entropy acquisition for Bayesian OptimizationCode1
Operator Learning with Gaussian ProcessesCode1
Model-Based Transfer Learning for Contextual Reinforcement LearningCode1
Gaussian process-based online health monitoring and fault analysis of lithium-ion battery systems from field dataCode1
A Rate-Distortion View of Uncertainty QuantificationCode1
Spatio-Temporal Attention and Gaussian Processes for Personalized Video Gaze EstimationCode1
Universal Functional Regression with Neural Operator FlowsCode1
A tutorial on learning from preferences and choices with Gaussian ProcessesCode1
Multi-Fidelity Residual Neural Processes for Scalable Surrogate ModelingCode1
Exact, Fast and Expressive Poisson Point Processes via Squared Neural FamiliesCode1
Standard Gaussian Process Can Be Excellent for High-Dimensional Bayesian OptimizationCode1
Self-Attention through Kernel-Eigen Pair Sparse Variational Gaussian ProcessesCode1
Conformal Approach To Gaussian Process Surrogate Evaluation With Coverage GuaranteesCode1
GP+: A Python Library for Kernel-based learning via Gaussian ProcessesCode1
Data-Driven Autoencoder Numerical Solver with Uncertainty Quantification for Fast Physical SimulationsCode1
Sound field reconstruction using neural processes with dynamic kernelsCode1
Solving High Frequency and Multi-Scale PDEs with Gaussian ProcessesCode1
Stochastic Gradient Descent for Gaussian Processes Done RightCode1
Thin and Deep Gaussian ProcessesCode1
A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics IdentificationCode1
Implicit Gaussian process representation of vector fields over arbitrary latent manifoldsCode1
Towards Efficient Modeling and Inference in Multi-Dimensional Gaussian Process State-Space ModelsCode1
A Unifying Variational Framework for Gaussian Process Motion PlanningCode1
BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decompositionCode1
GaPro: Box-Supervised 3D Point Cloud Instance Segmentation Using Gaussian Processes as Pseudo LabelersCode1
SEAL: Simultaneous Exploration and Localization in Multi-Robot SystemsCode1
Sampling from Gaussian Process Posteriors using Stochastic Gradient DescentCode1
Memory-Based Dual Gaussian Processes for Sequential LearningCode1
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Benchmark Results

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