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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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

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
SMT 2.0: A Surrogate Modeling Toolbox with a focus on Hierarchical and Mixed Variables Gaussian ProcessesCode2
Statistical Machine Learning for Astronomy -- A TextbookCode2
Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlowCode2
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
Convolutional Gaussian ProcessesCode2
L4acados: Learning-based models for acados, applied to Gaussian process-based predictive controlCode2
GAUCHE: A Library for Gaussian Processes in ChemistryCode2
Real-time Spatial-temporal Traversability Assessment via Feature-based Sparse Gaussian ProcessCode2
A Framework for Interdomain and Multioutput Gaussian ProcessesCode2
Gaussian Processes for Big DataCode2
GPflow: A Gaussian process library using TensorFlowCode2
MathOptAI.jl: Embed trained machine learning predictors into JuMP modelsCode2
Deep Pipeline Embeddings for AutoMLCode1
Bayesian Meta-Learning for the Few-Shot Setting via Deep KernelsCode1
Deep Random Features for Scalable Interpolation of Spatiotemporal DataCode1
Deep Gaussian Process Emulation using Stochastic ImputationCode1
Convolutional conditional neural processes for local climate downscalingCode1
Deep Gaussian Process-based Multi-fidelity Bayesian Optimization for Simulated Chemical ReactorsCode1
Deep Kernel LearningCode1
Data-Driven Autoencoder Numerical Solver with Uncertainty Quantification for Fast Physical SimulationsCode1
An Intuitive Tutorial to Gaussian Process RegressionCode1
Accounting for Input Noise in Gaussian Process Parameter RetrievalCode1
70 years of machine learning in geoscience in reviewCode1
Applications of Gaussian Processes at Extreme Lengthscales: From Molecules to Black HolesCode1
DeepKriging: Spatially Dependent Deep Neural Networks for Spatial PredictionCode1
A Rate-Distortion View of Uncertainty QuantificationCode1
Deep Reinforcement Learning for Human-Like Driving Policies in Collision Avoidance Tasks of Self-Driving CarsCode1
Causal Discovery via Bayesian OptimizationCode1
Calibrating Transformers via Sparse Gaussian ProcessesCode1
Conditional Neural ProcessesCode1
Bayes-Newton Methods for Approximate Bayesian Inference with PSD GuaranteesCode1
A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics IdentificationCode1
Variational multiple shooting for Bayesian ODEs with Gaussian processesCode1
BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decompositionCode1
Conditioning Sparse Variational Gaussian Processes for Online Decision-makingCode1
Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual InformationCode1
Bayesian Optimization of Catalysis With In-Context LearningCode1
Bayesian Optimization of Function NetworksCode1
Active Bayesian Causal InferenceCode1
Building 3D Morphable Models from a Single ScanCode1
Bayesian Deep Ensembles via the Neural Tangent KernelCode1
Batched Energy-Entropy acquisition for Bayesian OptimizationCode1
Constrained Causal Bayesian OptimizationCode1
Convergence of Sparse Variational Inference in Gaussian Processes RegressionCode1
Pre-trained Gaussian Processes for Bayesian OptimizationCode1
Bayesian Active Learning with Fully Bayesian Gaussian ProcessesCode1
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Benchmark Results

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