Regression with an Ensemble of Noisy Base Functions

Yuval Ben-Hur, Yuval Cassuto, Israel Cohen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publication2022 IEEE 32nd International Workshop on Machine Learning for Signal Processing, MLSP 2022
ISBN (Electronic)9781665485470
DOIs
StatePublished - 2022
Event32nd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2022 - Xi'an, China
Duration: 22 Aug 202225 Aug 2022

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing, MLSP
Volume2022-August

Conference

Conference32nd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2022
Country/TerritoryChina
CityXi'an
Period22/08/2225/08/22

Keywords

  • Ensemble learning
  • distributed regression
  • inference noise

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Human-Computer Interaction

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