Resource-aware Distributed Gaussian Process Regression for Real-time Machine Learning (original) (raw)

Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems

Quang Hoang

Proceedings of the AAAI Conference on Artificial Intelligence, 2019

View PDFchevron_right

Learning of Gaussian Processes in Distributed and Communication Limited Systems

Mostafa Tavassolipour

IEEE Transactions on Pattern Analysis and Machine Intelligence

View PDFchevron_right

Asynchronous Distributed Variational Gaussian Process for Regression

Shandian Zhe

2017

View PDFchevron_right

Consistent Online Gaussian Process Regression Without the Sample Complexity Bottleneck

Alec Koppel

2019 American Control Conference (ACC), 2019

View PDFchevron_right

Distributed Gaussian Processes

Jun Quan Ng

2015

View PDFchevron_right

Fast Gaussian Process Regression for Big Data

Rajiv Sambasivan

Big Data Research

View PDFchevron_right

Distributed Gaussian Process Regression Under Localization Uncertainty

Jongeun Choi

Journal of Dynamic Systems, Measurement, and Control, 2014

View PDFchevron_right

Hierarchical Mixture-of-Experts Model for Large-Scale Gaussian Process Regression

Marc Deisenroth

View PDFchevron_right

Correlated Product of Experts for Sparse Gaussian Process Regression

Manuel Schürch

Cornell University - arXiv, 2021

View PDFchevron_right

Efficient Gaussian process regression for large datasets

David Dunson

Biometrika, 2013

View PDFchevron_right

Recursive estimation for sparse Gaussian process regression

Manuel Schürch

Automatica, 2020

View PDFchevron_right

Ensemble Kalman Filtering for Online Gaussian Process Regression and Learning

Lyudmila Mihaylova

2018 21st International Conference on Information Fusion (FUSION)

View PDFchevron_right

Distributed Gaussian process regression for mobile sensor networks under localization uncertainty

Jongeun Choi

52nd IEEE Conference on Decision and Control, 2013

View PDFchevron_right

Scaling-Up Distributed Processing of Data Streams for Machine Learning

Haroon Raja

Proceedings of the IEEE, 2020

View PDFchevron_right

A data parallel approach for large-scale Gaussian process modeling

A. Choudhury

Proceedings of the Second SIAM …, 2002

View PDFchevron_right

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning

Anis Elgabli

J. Mach. Learn. Res., 2020

View PDFchevron_right

On the relationship between online Gaussian process regression and kernel least mean squares algorithms

Steven Van Vaerenbergh

2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), 2016

View PDFchevron_right

Collaborative Nonstationary Multivariate Gaussian Process Model

Kristofer Bouchard

ArXiv, 2021

View PDFchevron_right

Learning curves for multi-task Gaussian process regression

Peter Sollich

View PDFchevron_right

Scalable High-Order Gaussian Process Regression

Shandian Zhe

2019

View PDFchevron_right

Online Sparse Multi-Output Gaussian Process Regression and Learning

Lyudmila S Mihaylova

IEEE Transactions on Signal and Information Processing over Networks

View PDFchevron_right

Filtered Gaussian Processes for Learning with Large Data-Sets

Barak Pearlmutter, Roderick Murray-Smith

2005

View PDFchevron_right

Efficient Optimization for Sparse Gaussian Process Regression

Marcus Brubaker

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015

View PDFchevron_right

A Framework for Evaluating Approximation Methods for Gaussian Process Regression

Iain Murray

2012

View PDFchevron_right

Exact learning curves for Gaussian process regression on large random graphs

Peter Sollich

2010

View PDFchevron_right

Robust and Communication-Efficient Collaborative Learning

hamed hassan

arXiv (Cornell University), 2019

View PDFchevron_right

Approximation of Gaussian Process Regression Models after Training

Christian Igel

2008

View PDFchevron_right

Distributed Kernel Regression: An Algorithm for Training Collaboratively

project nd

Computing Research Repository, 2006

View PDFchevron_right