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

January 9, 2024

Deep Reinforcement Multi-agent Learning framework for Information Gathering with Local Gaussian Processes for Water Monitoring
Samuel Yanes Luis, Dmitriy Shutin, Juan Marchal Gómez, Daniel Gutiérrez Reina, Sergio Toral Marín
Deep Reinforcement Learning Gaussian Process Multi Agent Deep Reinforcement Learning Autonomous Surface Vehicle Water Quality Information Acquisition Double Deep Q Learning

December 22, 2023

Sample Path Regularity of Gaussian Processes from the Covariance Kernel
Nathaël Da Costa, Marvin Pförtner, Lancelot Da Costa, Philipp Hennig
Gaussian Process Covariance Kernel Sample Path

December 19, 2023

December 18, 2023

Domain Invariant Learning for Gaussian Processes and Bayesian Exploration
Xilong Zhao, Siyuan Bian, Yaoyun Zhang, Yuliang Zhang, Qinying Gu, Xinbing Wang, Chenghu Zhou, Nanyang Ye
Gaussian Process Bayesian Optimization Probabilistic Model Domain Invariant Invariant Learning Method

December 16, 2023

Uncertainty Quantification in Heterogeneous Treatment Effect Estimation with Gaussian-Process-Based Partially Linear Model
Shunsuke Horii, Yoichi Chikahara
Gaussian Process Uncertainty Quantification Treatment Effect Heterogeneous Treatment Effect Prior Distribution Semi Parametric Heterogeneous Treatment Effect Estimation Bayesian Inference Framework

December 15, 2023

Video-based Surgical Skill Assessment using Tree-based Gaussian Process Classifier
Arefeh Rezaei, Mohammad Javad Ahmadi, Amir Molaei, Hamid. D. Taghirad
Convolutional Neural Network Gaussian Process Surgical Skill Assessment Gesture Data

December 13, 2023

Meta-learning to Calibrate Gaussian Processes with Deep Kernels for Regression Uncertainty Estimation
Tomoharu Iwata, Atsutoshi Kumagai
Gaussian Process Deep Kernel Regression Uncertainty Estimation

December 12, 2023

GP+: A Python Library for Kernel-based learning via Gaussian Processes
Amin Yousefpour, Zahra Zanjani Foumani, Mehdi Shishehbor, Carlos Mora, Ramin Bostanabad
Gaussian Process Python Library Kernel Based Covariance Estimation Probabilistic Learning

December 9, 2023

Sparse Variational Student-t Processes
Jian Xu, Delu Zeng
Gaussian Process Sparse Representation Bayesian Learning Sparse Gaussian Process Sparse Variational Gaussian Process

December 8, 2023

Decoding Mean Field Games from Population and Environment Observations By Gaussian Processes
Jinyan Guo, Chenchen Mou, Xianjin Yang, Chao Zhou
Gaussian Process Environment Feature Bayesian Perspective Mean Field Game Na\"ive Population Noisy Observation

December 5, 2023

Active Learning for Abrupt Shifts Change-point Detection via Derivative-Aware Gaussian Processes
Hao Zhao, Rong Pan
Active Learning Gaussian Process Change Detection Change Point Detection Derivative Process

December 3, 2023

Regret Optimality of GP-UCB
Wenjia Wang, Xiaowei Zhang, Lu Zou
Gaussian Process Bayesian Optimization Cumulative Regret Upper Bound UCB Algorithm Tighter Bound

December 1, 2023

Scalable Meta-Learning with Gaussian Processes
Petru Tighineanu, Lukas Grossberger, Paul Baireuther, Kathrin Skubch, Stefan Falkner, Julia Vinogradska, Felix Berkenkamp
Gaussian Process Bayesian Optimization Meta Learning Multiple Kernel

November 29, 2023

A Probabilistic Method to Predict Classifier Accuracy on Larger Datasets given Small Pilot Data
Ethan Harvey, Wansu Chen, David M. Kent, Michael C. Hughes
Gaussian Process Classification Performance Pilot Study Large Datasets Probabilistic Approach Trend Extrapolation Learning Classifier

November 28, 2023

November 21, 2023

Variational Elliptical Processes
Maria Bånkestad, Jens Sjölund, Jalil Taghia, Thomas B. Schöon
Gaussian Process Variational Inference Sparse Variational Gaussian Process Elliptical Distribution

November 19, 2023

BOIS: Bayesian Optimization of Interconnected Systems
Leonardo D. González, Victor M. Zavala
Gaussian Process Bayesian Optimization Global Optimization

November 18, 2023

Short-term Volatility Estimation for High Frequency Trades using Gaussian processes (GPs)
Leonard Mushunje, Maxwell Mashasha, Edina Chandiwana
Gaussian Process High Frequency Market Volatility Volatility Forecasting Stochastic Volatility

Gaussian Process

Papers

Deep Reinforcement Multi-agent Learning framework for Information Gathering with Local Gaussian Processes for Water Monitoring

Sample Path Regularity of Gaussian Processes from the Covariance Kernel

Longitudinal prediction of DNA methylation to forecast epigenetic outcomes

Gaussian process learning of nonlinear dynamics

Domain Invariant Learning for Gaussian Processes and Bayesian Exploration

Uncertainty Quantification in Heterogeneous Treatment Effect Estimation with Gaussian-Process-Based Partially Linear Model

Video-based Surgical Skill Assessment using Tree-based Gaussian Process Classifier

Meta-learning to Calibrate Gaussian Processes with Deep Kernels for Regression Uncertainty Estimation

GP+: A Python Library for Kernel-based learning via Gaussian Processes

Sparse Variational Student-t Processes

Decoding Mean Field Games from Population and Environment Observations By Gaussian Processes

Active Learning for Abrupt Shifts Change-point Detection via Derivative-Aware Gaussian Processes

Regret Optimality of GP-UCB

Scalable Meta-Learning with Gaussian Processes

A Probabilistic Method to Predict Classifier Accuracy on Larger Datasets given Small Pilot Data

Computational Hypergraph Discovery, a Gaussian Process framework for connecting the dots

Gaussian Processes for Monitoring Air-Quality in Kampala

Variational Elliptical Processes

BOIS: Bayesian Optimization of Interconnected Systems

Short-term Volatility Estimation for High Frequency Trades using Gaussian processes (GPs)