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 451–500 of 1963 papers

TitleStatusHype
Uncertainty Informed Optimal Resource Allocation with Gaussian Process based Bayesian Inference—0
Spatiotemporal Besov Priors for Bayesian Inverse Problems—0
Evaluation of machine learning architectures on the quantification of epistemic and aleatoric uncertainties in complex dynamical systems—0
SEAL: Simultaneous Exploration and Localization in Multi-Robot SystemsCode1
Sampling from Gaussian Process Posteriors using Stochastic Gradient DescentCode1
A Bayesian Take on Gaussian Process NetworksCode0
Efficient Large-scale Nonstationary Spatial Covariance Function Estimation Using Convolutional Neural NetworksCode0
Time-Varying Transition Matrices with Multi-task Gaussian Processes—0
Spatio-temporal DeepKriging for Interpolation and Probabilistic Forecasting—0
Amortized Inference for Gaussian Process Hyperparameters of Structured KernelsCode0
Functional Causal Bayesian Optimization—0
Monte Carlo inference for semiparametric Bayesian regression—0
Representing and Learning Functions Invariant Under Crystallographic Groups—0
Training-Free Neural Active Learning with Initialization-Robustness GuaranteesCode0
Vehicle Dynamics Modeling for Autonomous Racing Using Gaussian Processes—0
Graph Classification Gaussian Processes via Spectral Features—0
Memory-Based Dual Gaussian Processes for Sequential LearningCode1
Global universal approximation of functional input maps on weighted spacesCode0
Constrained Causal Bayesian OptimizationCode1
Taylorformer: Probabilistic Modelling for Random Processes including Time SeriesCode0
Graph Neural Processes for Spatio-Temporal ExtrapolationCode1
A Learning-based Nonlinear Model Predictive Controller for a Real Go-Kart based on Black-box Dynamics Modeling through Gaussian Processes—0
On Neural Networks as Infinite Tree-Structured Probabilistic Graphical ModelsCode0
Vecchia Gaussian Process Ensembles on Internal Representations of Deep Neural Networks—0
Gaussian Processes with State-Dependent Noise for Stochastic Control—0
Density Ratio Estimation-based Bayesian Optimization with Semi-Supervised Learning—0
Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models—0
Deep Pipeline Embeddings for AutoMLCode1
SMT 2.0: A Surrogate Modeling Toolbox with a focus on Hierarchical and Mixed Variables Gaussian ProcessesCode2
MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under non-parameterized geometrical variabilityCode0
Physics Inspired Approaches To Understanding Gaussian ProcessesCode1
Posterior Inference on Shallow Infinitely Wide Bayesian Neural Networks under Weights with Unbounded VarianceCode0
Dynamic Term Structure Models with Nonlinearities using Gaussian Processes—0
Quantum neural networks form Gaussian processes—0
Learning Switching Port-Hamiltonian Systems with Uncertainty Quantification—0
Meta-models for transfer learning in source localisation—0
NUBO: A Transparent Python Package for Bayesian OptimizationCode1
Disentangled Multi-Fidelity Deep Bayesian Active LearningCode1
Optimizing Hyperparameters with Conformal Quantile RegressionCode1
Representing Additive Gaussian Processes by Sparse Matrices—0
A Chain Rule for the Expected Suprema of Bernoulli Processes—0
Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes—0
Mixtures of Gaussian process experts based on kernel stick-breaking processesCode0
Quantum Gaussian Process Regression for Bayesian Optimization—0
Stochastic MPC for energy hubs using data driven demand forecasting—0
Self-Correcting Bayesian Optimization through Bayesian Active Learning—0
Martingale Posterior Neural Processes—0
Neural networks for geospatial data—0
Promises and Pitfalls of the Linearized Laplace in Bayesian OptimizationCode0
Dimensionality Reduction as Probabilistic Inference—0
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Benchmark Results

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