Easy Learning from Label Proportions
- URL: http://arxiv.org/abs/2302.03115v1
- Date: Mon, 6 Feb 2023 20:41:38 GMT
- Title: Easy Learning from Label Proportions
- Authors: Robert Istvan Busa-Fekete, Heejin Choi, Travis Dick, Claudio Gentile,
Andres Munoz medina
- Abstract summary: Easyllp is a flexible and simple-to-implement debiasing approach based on aggregate labels.
Our technique allows us to accurately estimate the expected loss of an arbitrary model at an individual level.
- Score: 17.71834385754893
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We consider the problem of Learning from Label Proportions (LLP), a weakly
supervised classification setup where instances are grouped into "bags", and
only the frequency of class labels at each bag is available. Albeit, the
objective of the learner is to achieve low task loss at an individual instance
level. Here we propose Easyllp: a flexible and simple-to-implement debiasing
approach based on aggregate labels, which operates on arbitrary loss functions.
Our technique allows us to accurately estimate the expected loss of an
arbitrary model at an individual level. We showcase the flexibility of our
approach by applying it to popular learning frameworks, like Empirical Risk
Minimization (ERM) and Stochastic Gradient Descent (SGD) with provable
guarantees on instance level performance. More concretely, we exhibit a
variance reduction technique that makes the quality of LLP learning deteriorate
only by a factor of k (k being bag size) in both ERM and SGD setups, as
compared to full supervision. Finally, we validate our theoretical results on
multiple datasets demonstrating our algorithm performs as well or better than
previous LLP approaches in spite of its simplicity.
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