Gaussian Processes
567 papers with code • 1 benchmarks • 5 datasets
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.
Benchmarks
These leaderboards are used to track progress in Gaussian Processes
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
Libraries
Use these libraries to find Gaussian Processes models and implementations
UCI Machine Learning Repository
MPI FAUST Dataset
Poser
Latest papers with no code
Analytical results for uncertainty propagation through trained machine learning regression models
Machine learning (ML) models are increasingly being used in metrology applications.
BayesJudge: Bayesian Kernel Language Modelling with Confidence Uncertainty in Legal Judgment Prediction
Predicting legal judgments with reliable confidence is paramount for responsible legal AI applications.
Label Propagation Training Schemes for Physics-Informed Neural Networks and Gaussian Processes
This paper proposes a semi-supervised methodology for training physics-informed machine learning methods.
Conditioning of Banach Space Valued Gaussian Random Variables: An Approximation Approach Based on Martingales
In this paper we investigate the conditional distributions of two Banach space valued, jointly Gaussian random variables.
Universal Functional Regression with Neural Operator Flows
We empirically study the performance of OpFlow on regression and generation tasks with data generated from Gaussian processes with known posterior forms and non-Gaussian processes, as well as real-world earthquake seismograms with an unknown closed-form distribution.
Tensor Network-Constrained Kernel Machines as Gaussian Processes
We analyze the convergence of both CPD and TT-constrained models, and show how TT yields models exhibiting more GP behavior compared to CPD, for the same number of model parameters.
A Unified Kernel for Neural Network Learning
Two predominant approaches have emerged: the Neural Network Gaussian Process (NNGP) and the Neural Tangent Kernel (NTK).
Multi-Agent Clarity-Aware Dynamic Coverage with Gaussian Processes
This paper presents two algorithms for multi-agent dynamic coverage in spatiotemporal environments, where the coverage algorithms are informed by the method of data assimilation.
Learning Piecewise Residuals of Control Barrier Functions for Safety of Switching Systems using Multi-Output Gaussian Processes
This uncertainty results in piecewise residuals for each switching surface, impacting the CLF and CBF constraints.
Guided Bayesian Optimization: Data-Efficient Controller Tuning with Digital Twin
This article presents the guided Bayesian optimization algorithm as an efficient data-driven method for iteratively tuning closed-loop controller parameters using an event-triggered digital twin of the system based on available closed-loop data.
