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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 201–225 of 1963 papers

TitleStatusHype
Sparse Inducing Points in Deep Gaussian Processes: Enhancing Modeling with Denoising Diffusion Variational InferenceCode0
Practical multi-fidelity machine learning: fusion of deterministic and Bayesian modelsCode0
Data-Driven Abstractions via Binary-Tree Gaussian Processes for Formal Verification—0
Inference at the data's edge: Gaussian processes for modeling and inference under model-dependency, poor overlap, and extrapolation—0
Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian ProcessesCode0
Genus expansion for non-linear random matrix ensembles with applications to neural networks—0
The GeometricKernels Package: Heat and Matérn Kernels for Geometric Learning on Manifolds, Meshes, and GraphsCode4
Implementation and Analysis of GPU Algorithms for Vecchia ApproximationCode0
Scalable Multi-Output Gaussian Processes with Stochastic Variational Inference—0
Adaptive RKHS Fourier Features for Compositional Gaussian Process ModelsCode0
Diffusion-BBO: Diffusion-Based Inverse Modeling for Online Black-Box Optimization—0
DADEE: Well-calibrated uncertainty quantification in neural networks for barriers-based robot safety—0
Learning Time-Varying Multi-Region Communications via Scalable Markovian Gaussian Processes—0
Permutation invariant multi-output Gaussian Processes for drug combination prediction in cancer—0
Gaussian process-based online health monitoring and fault analysis of lithium-ion battery systems from field dataCode1
Data-driven identification of port-Hamiltonian DAE systems by Gaussian processes—0
Probabilistic Subgoal Representations for Hierarchical Reinforcement learningCode0
BrowNNe: Brownian Nonlocal Neurons & Activation Functions—0
Bayesian Circular Regression with von Mises Quasi-Processes—0
A Rate-Distortion View of Uncertainty QuantificationCode1
Marginalization Consistent Probabilistic Forecasting of Irregular Time Series via Mixture of Separable flows—0
On Learning what to Learn: heterogeneous observations of dynamics and establishing (possibly causal) relations among them—0
On the Consistency of Kernel Methods with Dependent Observations—0
Linearization Turns Neural Operators into Function-Valued Gaussian Processes—0
Approximation-Aware Bayesian Optimization—0
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Benchmark Results

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