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

March 3, 2022

Debiased Batch Normalization via Gaussian Process for Generalizable Person Re-Identification
Jiawei Liu, Zhipeng Huang, Liang Li, Kecheng Zheng, Zheng-Jun Zha
Gaussian Process Batch Normalization Unseen Domain Generalizable Person Re Identification

March 2, 2022

Scalable Bayesian Optimization Using Vecchia Approximations of Gaussian Processes
Felix Jimenez, Matthias Katzfuss
Gaussian Process Bayesian Optimization Surrogate Model First Order Bayesian Optimization

February 28, 2022

GPU-Accelerated Policy Optimization via Batch Automatic Differentiation of Gaussian Processes for Real-World Control
Abdolreza Taheri, Joni Pajarinen, Reza Ghabcheloo
Gaussian Process Policy OpTimization Policy Optimization Automatic Differentiation Policy Network Real World Control

February 24, 2022

AutoIP: A United Framework to Integrate Physics into Gaussian Processes
Da Long, Zheng Wang, Aditi Krishnapriyan, Robert Kirby, Shandian Zhe, Michael Mahoney
Gaussian Process Uncertainty Quantification Theoretical Physic Data Driven Model Physical Model Stochastic Variational Inference

February 23, 2022

Networked Online Learning for Control of Safety-Critical Resource-Constrained Systems based on Gaussian Processes
Armin Lederer, Mingmin Zhang, Samuel Tesfazgi, Sandra Hirche
Gaussian Process External Control RSD Difference of Gaussian Gaussian Process Regression Safety Critical System Online Inference Safety Critical Application

February 22, 2022

February 11, 2022

February 9, 2022

Adjoint-aided inference of Gaussian process driven differential equations
Paterne Gahungu, Christopher W Lanyon, Mauricio A Alvarez, Engineer Bainomugisha, Michael Smith, Richard D. Wilkinson
Gaussian Process Scientific Inference Differential Equation Bayesian Optimisation Exact Bayesian Inference Basis Expansion

February 8, 2022

Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning
Sattar Vakili, Jonathan Scarlett, Da-shan Shiu, Alberto Bernacchia
Gaussian Process Convergence Rate Kernel Ridge Regression Kernel Based Sparse Approximation Sparse Variational Gaussian Process

February 7, 2022

Gaussian Graphical Models as an Ensemble Method for Distributed Gaussian Processes
Hamed Jalali, Gjergji Kasneci
Gaussian Process Ensemble Method Conditional Distribution Global Forecasting Gaussian Graphical Model

February 3, 2022

January 30, 2022

Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times
Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco
Gaussian Process Sequential Optimization

January 18, 2022

Online Time Series Anomaly Detection with State Space Gaussian Processes
Christian Bock, François-Xavier Aubet, Jan Gasthaus, Andrey Kan, Ming Chen, Laurent Callot
Anomaly Detection Gaussian Process Multivariate Time Series High Dimensional Time Series Gaussian Process State Space Model Gaussian Process Factor Analysis

January 13, 2022

Density reconstruction from schlieren images through Bayesian nonparametric models
Bryn Noel Ubald, Pranay Seshadri, Andrew Duncan
Gaussian Process Density Estimation Quantitative Analysis Bayesian Nonparametric Density Reconstruction Schlieren Image

Gaussian Process

Papers

Debiased Batch Normalization via Gaussian Process for Generalizable Person Re-Identification

Scalable Bayesian Optimization Using Vecchia Approximations of Gaussian Processes

GPU-Accelerated Policy Optimization via Batch Automatic Differentiation of Gaussian Processes for Real-World Control

Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference

Learning Multi-Task Gaussian Process Over Heterogeneous Input Domains

Learning Invariant Weights in Neural Networks

AutoIP: A United Framework to Integrate Physics into Gaussian Processes

Networked Online Learning for Control of Safety-Critical Resource-Constrained Systems based on Gaussian Processes

Adaptive Cholesky Gaussian Processes

Gaussian Processes and Statistical Decision-making in Non-Euclidean Spaces

Fast Model-based Policy Search for Universal Policy Networks

The Schr\"odinger Bridge between Gaussian Measures has a Closed Form

Adjoint-aided inference of Gaussian process driven differential equations

Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning

Gaussian Graphical Models as an Ensemble Method for Distributed Gaussian Processes

Incorporating Sum Constraints into Multitask Gaussian Processes

Variational Nearest Neighbor Gaussian Process

Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times

Online Time Series Anomaly Detection with State Space Gaussian Processes

Density reconstruction from schlieren images through Bayesian nonparametric models