Summary of Learning From Label Proportions and Covariate-shifted Instances, by Sagalpreet Singh et al.
Learning from Label Proportions and Covariate-shifted Instances
by Sagalpreet Singh, Navodita Sharma, Shreyas Havaldar, Rishi Saket, Aravindan Raghuveer
First submitted to arxiv on: 19 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 In this paper, researchers tackle a challenging problem in machine learning called “covariate-shifted hybrid LLP.” They propose novel methods to train models that can predict instance-level labels in the target domain when only aggregate bag-labels are available. The approach leverages fully supervised covariate-shifted source data and incorporates target bag-labels along with source instance-labels in a domain adaptation framework. The paper provides theoretical guarantees for bounding the target generalization error and experimental results on several publicly available datasets, showing that the proposed methods outperform existing baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, scientists try to find a way to teach machines to predict what’s inside individual bags of data when all we have is information about the whole bag. They come up with new techniques that use extra training data and combine it with the bag labels from the real-world data. The goal is to make predictions better than before. The researchers prove that their methods work well and test them on some public datasets, showing they’re more accurate than other approaches. |
Keywords
» Artificial intelligence » Domain adaptation » Generalization » Machine learning » Supervised
