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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 351–375 of 1963 papers

TitleStatusHype
Meta-learning to Calibrate Gaussian Processes with Deep Kernels for Regression Uncertainty Estimation—0
Wiener Chaos in Kernel Regression: Towards Untangling Aleatoric and Epistemic Uncertainty—0
GP+: A Python Library for Kernel-based learning via Gaussian ProcessesCode1
Sparse Variational Student-t Processes—0
Decoding Mean Field Games from Population and Environment Observations By Gaussian Processes—0
Safe Stabilization with Model Uncertainties: A Universal Formula with Gaussian Process Learning—0
Active Learning for Abrupt Shifts Change-point Detection via Derivative-Aware Gaussian Processes—0
Data-Driven Autoencoder Numerical Solver with Uncertainty Quantification for Fast Physical SimulationsCode1
Scalable Meta-Learning with Gaussian Processes—0
Estimation of Dynamic Gaussian ProcessesCode0
Gaussian Processes for Monitoring Air-Quality in KampalaCode0
From Prediction to Action: Critical Role of Performance Estimation for Machine-Learning-Driven Materials Discovery—0
Controllable Expensive Multi-objective Learning with Warm-starting Bayesian Optimization—0
Variational Elliptical Processes—0
BOIS: Bayesian Optimization of Interconnected Systems—0
Short-term Volatility Estimation for High Frequency Trades using Gaussian processes (GPs)—0
Spatial Bayesian Neural NetworksCode0
A Gaussian Process Based Method with Deep Kernel Learning for Pricing High-dimensional American Options—0
High-dimensional mixed-categorical Gaussian processes with application to multidisciplinary design optimization for a green aircraftCode2
Sound field reconstruction using neural processes with dynamic kernelsCode1
Solving High Frequency and Multi-Scale PDEs with Gaussian ProcessesCode1
Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor DataCode0
Kernel-, mean- and noise-marginalised Gaussian processes for exoplanet transits and H_0 inferenceCode0
SemiGPC: Distribution-Aware Label Refinement for Imbalanced Semi-Supervised Learning Using Gaussian Processes—0
Neural SPDE solver for uncertainty quantification in high-dimensional space-time dynamics—0
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Benchmark Results

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