Feature Multi-Selection among Subjective Features

Sivan Sabato, Adam Kalai

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


When dealing with subjective, noisy, or otherwise nebulous features, the "wisdom of crowds" suggests that one may benefit from multiple judgments of the same feature on the same object. We give theoretically- motivated feature multi-selection algorithms that choose, among a large set of candidate features, not only which features to judge but how many times to judge each one. We demonstrate the effectiveness of this approach for linear regression on a crowd-sourced learning task of predicting people's height and weight from photos, using features such as gender and estimated weight as well as culturally fraught ones such as attractive.

Original languageEnglish GB
Title of host publicationProceedings of the 30th International Conference on Machine Learning (ICML), JMLR Workshop and Conference Proceedings
StatePublished - 2013
Externally publishedYes
Event30th International Conference on Machine Learning, ICML 2013 - Atlanta, GA, United States
Duration: 16 Jun 201321 Jun 2013


Conference30th International Conference on Machine Learning, ICML 2013
Country/TerritoryUnited States
CityAtlanta, GA

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Sociology and Political Science


Dive into the research topics of 'Feature Multi-Selection among Subjective Features'. Together they form a unique fingerprint.

Cite this