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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 51–75 of 1963 papers

TitleStatusHype
Actually Sparse Variational Gaussian ProcessesCode1
Pre-trained Gaussian Processes for Bayesian OptimizationCode1
Accounting for Input Noise in Gaussian Process Parameter RetrievalCode1
Time series forecasting with Gaussian Processes needs priorsCode1
Deep Gaussian Process Emulation using Stochastic ImputationCode1
Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual InformationCode1
Bayesian Active Learning with Fully Bayesian Gaussian ProcessesCode1
Batched Energy-Entropy acquisition for Bayesian OptimizationCode1
Bayesian Deep Learning and a Probabilistic Perspective of GeneralizationCode1
Bayesian Deep Ensembles via the Neural Tangent KernelCode1
Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian ProcessesCode1
Active Bayesian Causal InferenceCode1
Deep Kernel LearningCode1
Data-Driven Autoencoder Numerical Solver with Uncertainty Quantification for Fast Physical SimulationsCode1
Convergence of Sparse Variational Inference in Gaussian Processes RegressionCode1
Deep Gaussian Process-based Multi-fidelity Bayesian Optimization for Simulated Chemical ReactorsCode1
Bayesian Meta-Learning for the Few-Shot Setting via Deep KernelsCode1
Conditional Neural ProcessesCode1
Calibrating Transformers via Sparse Gaussian ProcessesCode1
Building 3D Morphable Models from a Single ScanCode1
An Intuitive Tutorial to Gaussian Process RegressionCode1
Constrained Causal Bayesian OptimizationCode1
Convolutional conditional neural processes for local climate downscalingCode1
Causal Discovery via Bayesian OptimizationCode1
Conditioning Sparse Variational Gaussian Processes for Online Decision-makingCode1
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Benchmark Results

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