Gaussian Process

Gaussian processes (GPs) are probabilistic models used for function approximation and uncertainty quantification, offering a powerful framework for various applications. Current research focuses on extending GPs' capabilities through novel architectures like deep GPs and hybrid models combining GPs with neural networks or other machine learning techniques, addressing scalability and computational efficiency challenges, particularly in high-dimensional or time-varying settings. These advancements are significantly impacting fields like robotics, control systems, and scientific modeling by providing robust, uncertainty-aware predictions and enabling more reliable decision-making in complex systems. The development of efficient algorithms and theoretical analyses further enhances the practical applicability and trustworthiness of GP-based methods.

Papers

April 22, 2022

A piece-wise constant approximation for non-conjugate Gaussian Process models
Sarem Seitz
Gaussian Process Sparse Variational Gaussian Process Link Function

April 21, 2022

Inducing Gaussian Process Networks
Alessandro Tibo, Thomas Dyhre Nielsen
Gaussian Process Kernel Approximation Covariance Kernel

April 19, 2022

On the Dynamics of Inference and Learning
David S. Berman, Jonathan J. Heckman, Marc Klinger
LeArning Abstract Gaussian Process Scientific Inference Statistical Inference Information Geometry Bayesian Flow

April 18, 2022

Active Learning with Weak Supervision for Gaussian Processes
Amanda Olmin, Jakob Lindqvist, Lennart Svensson, Fredrik Lindsten
Active Learning Gaussian Process Supervised Learning Weak Supervision Active Learning Algorithm Annotation Budget Annotation Error Rate

April 11, 2022

RMFGP: Rotated Multi-fidelity Gaussian process with Dimension Reduction for High-dimensional Uncertainty Quantification
Jiahao Zhang, Shiqi Zhang, Guang Lin
Gaussian Process Dimension Reduction Multi Fidelity Multi Fidelity Gaussian Process High Dimensional Uncertainty

April 10, 2022

Gaussian Processes for Missing Value Imputation
Bahram Jafrasteh, Daniel Hernández-Lobato, Simón Pedro Lubián-López, Isabel Benavente-Fernández
Gaussian Process Imputation Algorithm Missing Value Sparse Approximation Missing Value Imputation

April 6, 2022

April 5, 2022

GP-BART: a novel Bayesian additive regression trees approach using Gaussian processes
Mateus Maia, Keefe Murphy, Andrew C. Parnell
Gaussian Process Regression Task Structural Prior Covariance Structure Bayesian Additive Regression Tree

March 29, 2022

Gaussian Control Barrier Functions : A Non-Parametric Paradigm to Safety
Mouhyemen Khan, Tatsuya Ibuki, Abhijit Chatterjee
Gaussian Process Control Barrier Function Human SAFETY CBF Based Robust Uncertainty

March 26, 2022

Probabilistic Registration for Gaussian Process 3D shape modelling in the presence of extensive missing data
Filipa Valdeira, Ricardo Ferreira, Alessandra Micheletti, Cláudia Soares
Gaussian Process Speech Presence Shape Model Shape Modeling Gaussian Process Implicit Surface Probabilistic Registration

March 24, 2022

Position Tracking using Likelihood Modeling of Channel Features with Gaussian Processes
Sebastian Kram, Christopher Kraus, Tobias Feigl, Maximilian Stahlke, Jörg Robert, Christopher Mutschler
Gaussian Process Channel Wise Likelihood Based Multipath Propagation

March 21, 2022

Fully-probabilistic Terrain Modelling with Stochastic Variational Gaussian Process Maps
Ignacio Torroba, Christopher Illife Sprague, John Folkesson
Gaussian Process Uncertainty Map Variational Gaussian Process Terrain Model P Map

March 17, 2022

March 15, 2022

Regret Bounds for Expected Improvement Algorithms in Gaussian Process Bandit Optimization
Hung Tran-The, Sunil Gupta, Santu Rana, Svetha Venkatesh
Gaussian Process Regret Bound Cumulative Regret Information Gain Gaussian Process Bandit Expected Improvement

March 14, 2022

March 9, 2022

Structure and Distribution Metric for Quantifying the Quality of Uncertainty: Assessing Gaussian Processes, Deep Neural Nets, and Deep Neural Operators for Regression
Ethan Pickering, Themistoklis P. Sapsis
Deep Neural Network Gaussian Process Novel Regression Regression Task Deep Neural Operator Ensemble Deep Minimum Swept Volume Metric Structure Metric Distribution

March 6, 2022

Fully Decentralized, Scalable Gaussian Processes for Multi-Agent Federated Learning
George P. Kontoudis, Daniel J. Stilwell
Gaussian Process Multi Agent System Consensus Based Approach

Gaussian Process

Papers

A piece-wise constant approximation for non-conjugate Gaussian Process models

Inducing Gaussian Process Networks

On the Dynamics of Inference and Learning

Active Learning with Weak Supervision for Gaussian Processes

RMFGP: Rotated Multi-fidelity Gaussian process with Dimension Reduction for High-dimensional Uncertainty Quantification

Gaussian Processes for Missing Value Imputation

Random Features Model with General Convex Regularization: A Fine Grained Analysis with Precise Asymptotic Learning Curves

PAGP: A physics-assisted Gaussian process framework with active learning for forward and inverse problems of partial differential equations

GP-BART: a novel Bayesian additive regression trees approach using Gaussian processes

Gaussian Control Barrier Functions : A Non-Parametric Paradigm to Safety

Probabilistic Registration for Gaussian Process 3D shape modelling in the presence of extensive missing data

Position Tracking using Likelihood Modeling of Channel Features with Gaussian Processes

Fully-probabilistic Terrain Modelling with Stochastic Variational Gaussian Process Maps

A Framework and Benchmark for Deep Batch Active Learning for Regression

Maximum Likelihood Estimation in Gaussian Process Regression is Ill-Posed

Regret Bounds for Expected Improvement Algorithms in Gaussian Process Bandit Optimization

On Connecting Deep Trigonometric Networks with Deep Gaussian Processes: Covariance, Expressivity, and Neural Tangent Kernel

On the Nash equilibrium of moment-matching GANs for stationary Gaussian processes

Structure and Distribution Metric for Quantifying the Quality of Uncertainty: Assessing Gaussian Processes, Deep Neural Nets, and Deep Neural Operators for Regression

Fully Decentralized, Scalable Gaussian Processes for Multi-Agent Federated Learning