Simplified and Unified Analysis of Various Learning Problems by Reduction to Multiple-Instance Learning

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In statistical learning, many problem formulations have been proposed so far, such as multi-class learning, complementarily labeled learning, multi-label learning, multi-task learning, which provide theoretical models for various real-world tasks. Although they have been extensively studied, the relationship among them has not been fully investigated. In this work, we focus on a particular problem formulation called Multiple-Instance Learning (MIL), and show that various learning problems including all the problems mentioned above with some of new problems can be reduced to MIL with theoretically guaranteed generalization bounds, where the reductions are established under a new reduction scheme we provide as a byproduct. The results imply that the MIL-reduction gives a simplified and unified framework for designing and analyzing algorithms for various learning problems. Moreover, we show that the MIL-reduction framework can be kernelized.

Original languageEnglish
Title of host publicationProceedings of the 38th Conference on Uncertainty in Artificial Intelligence, UAI 2022
PublisherAssociation For Uncertainty in Artificial Intelligence (AUAI)
Pages1896-1906
Number of pages11
ISBN (Electronic)9781713863298
Publication statusPublished - 2022
Event38th Conference on Uncertainty in Artificial Intelligence, UAI 2022 - Eindhoven, Netherlands
Duration: Aug 1 2022Aug 5 2022

Publication series

NameProceedings of the 38th Conference on Uncertainty in Artificial Intelligence, UAI 2022

Conference

Conference38th Conference on Uncertainty in Artificial Intelligence, UAI 2022
Country/TerritoryNetherlands
CityEindhoven
Period8/1/228/5/22

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

Fingerprint

Dive into the research topics of 'Simplified and Unified Analysis of Various Learning Problems by Reduction to Multiple-Instance Learning'. Together they form a unique fingerprint.

Cite this