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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 601–625 of 1963 papers

TitleStatusHype
Fully Bayesian inference for latent variable Gaussian process modelsCode0
Black-box Coreset Variational InferenceCode0
Fast and robust Bayesian Inference using Gaussian Processes with GPryCode1
Isotropic Gaussian Processes on Finite Spaces of GraphsCode0
Benefits of Monotonicity in Safe Exploration with Gaussian ProcessesCode0
Learning safety in model-based Reinforcement Learning using MPC and Gaussian ProcessesCode1
Fantasizing with Dual GPs in Bayesian Optimization and Active Learning—0
Monte Carlo Tree Descent for Black-Box Optimization—0
Deep Gaussian Process-based Multi-fidelity Bayesian Optimization for Simulated Chemical ReactorsCode1
Data-driven Output Regulation via Gaussian Processes and Luenberger Internal Models—0
Structural Kernel Search via Bayesian Optimization and Symbolical Optimal TransportCode0
Optimization on Manifolds via Graph Gaussian Processes—0
Uncertainty Disentanglement with Non-stationary Heteroscedastic Gaussian Processes for Active Learning—0
Scalable Bayesian Transformed Gaussian Processes—0
Locally Smoothed Gaussian Process Regression—0
Conditional Neural Processes for Molecules—0
Model of rough surfaces with Gaussian processes—0
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover TreesCode0
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes—0
Gaussian Processes on Distributions based on Regularized Optimal Transport—0
Inferring Smooth Control: Monte Carlo Posterior Policy Iteration with Gaussian ProcessesCode0
Understanding Neural Coding on Latent Manifolds by Sharing Features and Dividing EnsemblesCode0
Inference on Causal Effects of Interventions in Time using Gaussian Processes—0
Safety-Aware Learning-Based Control of Systems with Uncertainty Dependent Constraints (extended version)—0
Log-Linear-Time Gaussian Processes Using Binary Tree KernelsCode0
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Benchmark Results

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