Summary of A General Framework For Learning From Weak Supervision, by Hao Chen et al.
A General Framework for Learning from Weak Supervision
by Hao Chen, Jindong Wang, Lei Feng, Xiang Li, Yidong Wang, Xing Xie, Masashi Sugiyama, Rita Singh, Bhiksha Raj
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper presents a general framework for learning from weak supervision (GLWS) with a novel algorithm that can efficiently accommodate various types of weak supervision sources. The Expectation-Maximization (EM) formulation at the heart of GLWS enables it to handle instance partial labels, aggregate statistics, pairwise observations, and unlabeled data. To improve scalability, an advanced algorithm uses a Non-deterministic Finite Automaton (NFA) with a forward-backward algorithm, reducing time complexity from quadratic or factorial to linear scale. The GLWS framework not only enhances the scalability of machine learning models but also demonstrates superior performance and versatility across 11 weak supervision scenarios. This work has the potential to pave the way for practical deployment in this field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper introduces a new way to learn from incomplete information (weak supervision). The method can use different types of clues, such as partial labels or statistics about groups of data points. The authors also make their algorithm faster and more efficient by using a special type of mathematical model called a Non-deterministic Finite Automaton. This allows the algorithm to work with much larger amounts of data than before. The new method is shown to be better at learning from weak supervision and can handle many different types of clues. The goal of this research is to make it possible to use machine learning models in more real-world scenarios where data is incomplete or hard to come by. |
Keywords
* Artificial intelligence * Machine learning
