@inproceedings{c56529f060244e46bfd2022ddafa1cce,
title = "Regression with an Ensemble of Noisy Base Functions",
abstract = "Ensemble methods achieve state-of-the-art performance in many real-world regression problems while enjoying structural compatibility for modern decentralized computing architectures. However, the implementation of ensemble regression on distributed systems may compromise its cutting-edge performance due to computing and communication reliability issues. This paper introduces robust ensemble combining techniques designed to integrate multiple noisy predictions into a single reliable prediction. Experiments conducted with synthetic and real-world datasets in various noise regimes illustrate our robust methods' superiority over non-robust counterparts.",
keywords = "Ensemble learning, distributed regression, inference noise",
author = "Yuval Ben-Hur and Yuval Cassuto and Israel Cohen",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 32nd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2022 ; Conference date: 22-08-2022 Through 25-08-2022",
year = "2022",
doi = "10.1109/MLSP55214.2022.9943368",
language = "الإنجليزيّة",
series = "IEEE International Workshop on Machine Learning for Signal Processing, MLSP",
booktitle = "2022 IEEE 32nd International Workshop on Machine Learning for Signal Processing, MLSP 2022",
}