Summary of On Characterizing and Mitigating Imbalances in Multi-instance Partial Label Learning, by Kaifu Wang et al.
On Characterizing and Mitigating Imbalances in Multi-Instance Partial Label Learning
by Kaifu Wang, Efthymia Tsamoura, Dan Roth
First submitted to arxiv on: 13 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The Multi-Instance Partial Label Learning (MI-PLL) paper proposes a weakly-supervised learning setting that combines partial label learning, latent structural learning, and neurosymbolic learning. The authors introduce multiple contributions to address the problem of characterizing and mitigating learning imbalances in MI-PLL. They derive class-specific risk bounds for MI-PLL with minimal assumptions, revealing a unique phenomenon where the supervision signal can greatly impact learning imbalances. This is in contrast to previous research on supervised and weakly-supervised learning, which only studies learning imbalances under data imbalances. The paper also introduces techniques for estimating the marginal of hidden labels using MI-PLL data and algorithms that mitigate imbalances at training- and testing-time by treating the marginal as a constraint. Experimental results demonstrate performance improvements of up to 14% compared to strong baselines from neurosymbolic and long-tail learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new type of machine learning called Multi-Instance Partial Label Learning (MI-PLL). This is a way for computers to learn from incomplete or partially labeled data. The authors want to make sure that the computer doesn’t get biased towards certain types of information, which can happen when the training data is imbalanced. They developed a new technique to estimate how likely something is to be correctly classified based on its hidden label. This helps reduce bias and improve overall performance. The results show that this approach can lead to better predictions, with some models improving by up to 14%. |
Keywords
» Artificial intelligence » Machine learning » Supervised
