Learning from Label Proportions (LLP) Online ind Onl ine - PowerPoint PPT Presentation

Apr 09, 2023 •196 likes •239 views

(Almost) No Label No Cry Giorgio Patrini with R.Nock, P.Rivera, T.Caetano Learning from Label Proportions (LLP) Online ind Onl ine individual r ividual recor ecords ds Per Percent cent unemployment unemployment Input : unlabeled data

(Almost) No Label No Cry Giorgio Patrini with R.Nock, P.Rivera, T.Caetano Learning from Label Proportions (LLP) Online ind Onl ine individual r ividual recor ecords ds Per Percent cent unemployment unemployment Input : unlabeled data Input : label proportions Output : predictor of individual unemployment How likely Alice is unemployed given only her online behavior
(Almost) No Label No Cry Giorgio Patrini with R.Nock, P.Rivera, T.Caetano Our Solution Def, Altun&Smola ’06: the mean operator µ = 1 /m P m i =1 y i x i Thm: is su ffi cient for the label variable for most Proper Losses: µ proper-loss = loss w/o labels ( θ ) − 1 2 < θ , µ > • Quadrianto et al. ‘09, • Our relaxation: homogeneity assumption : “The more similar the counties, the more “Unemployed people in all the counties similar the online behavior of the behave online in the same way” unemployed people”
(Almost) No Label No Cry Giorgio Patrini with R.Nock, P.Rivera, T.Caetano Results • Finite sample approximation bounds for the resulting classifier (do not hold for previous approaches) • First generalization result for LLP , based on Rademacher complexity #proportions/#instances “more supervised”

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