Summary of Weak to Strong Learning From Aggregate Labels, by Yukti Makhija and Rishi Saket
Weak to Strong Learning from Aggregate Labels
by Yukti Makhija, Rishi Saket
First submitted to arxiv on: 9 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Structures and Algorithms (cs.DS); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning framework for training instance-level predictors is explored in this paper, focusing on learning from aggregate labels. The researchers investigate the problem of using weak learners to obtain strong learners when the training data consists of sets or “bags” of feature-vectors along with an aggregate label derived from the labels of its instances. The authors analyze boosting algorithms in multiple settings, including learning from label proportions (LLP) and multiple instance learning (MIL). Notably, they prove the impossibility of boosting in LLP and MIL for certain ranges of weak learner accuracy. However, they also demonstrate that a weak learner can be used to obtain a strong learner in the LLP setting for small bags. The authors provide an algorithm for achieving this transformation in polynomial time and validate their findings empirically on five datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper studies how machines learn from group labels instead of individual ones. It looks at different ways to train models that predict the label of each item based on a group’s label. The researchers show that even if you have a simple model that makes mistakes, it can still be used to create a better model in some cases. They also prove that there are situations where this process won’t work and demonstrate their findings with real-world data. |
Keywords
» Artificial intelligence » Boosting » Machine learning
