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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 101–125 of 1963 papers

TitleStatusHype
Deep Gaussian Process Emulation using Stochastic ImputationCode1
High-Dimensional Gaussian Process Inference with DerivativesCode1
Actually Sparse Variational Gaussian ProcessesCode1
Building 3D Morphable Models from a Single ScanCode1
On Feature Collapse and Deep Kernel Learning for Single Forward Pass UncertaintyCode1
Improving Pareto Set Learning for Expensive Multi-objective Optimization via Stein Variational HypernetworksCode1
An Intuitive Tutorial to Gaussian Process RegressionCode1
Deep Kernel LearningCode1
Kernel Interpolation for Scalable Online Gaussian ProcessesCode1
Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP)Code1
Bayesian Optimization of Function NetworksCode1
Light curve completion and forecasting using fast and scalable Gaussian processes (MuyGPs)Code1
Gaussian process-based online health monitoring and fault analysis of lithium-ion battery systems from field dataCode1
Low-Precision Arithmetic for Fast Gaussian ProcessesCode1
Memory-Based Dual Gaussian Processes for Sequential LearningCode1
Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property PredictionCode1
Conditional Neural ProcessesCode1
Conditioning Sparse Variational Gaussian Processes for Online Decision-makingCode1
Constrained Causal Bayesian OptimizationCode1
Convergence of Sparse Variational Inference in Gaussian Processes RegressionCode1
MOGPTK: The Multi-Output Gaussian Process ToolkitCode1
Multi-class Gaussian Process Classification with Noisy InputsCode1
Convolutional conditional neural processes for local climate downscalingCode1
A Rate-Distortion View of Uncertainty QuantificationCode1
Deep Gaussian Process-based Multi-fidelity Bayesian Optimization for Simulated Chemical ReactorsCode1
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Benchmark Results

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