Summary of Weakly Supervised Learners For Correction Of Ai Errors with Provable Performance Guarantees, by Ivan Y. Tyukin et al.
Weakly Supervised Learners for Correction of AI Errors with Provable Performance Guarantees
by Ivan Y. Tyukin, Tatiana Tyukina, Daniel van Helden, Zedong Zheng, Evgeny M. Mirkes, Oliver J. Sutton, Qinghua Zhou, Alexander N. Gorban, Penelope Allison
First submitted to arxiv on: 31 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper introduces a novel approach for handling AI errors by developing weakly supervised AI error correctors with a priori performance guarantees. These auxiliary maps moderate the decisions of underlying classifiers, either approving or rejecting them, and can be used to suggest abstaining from making a decision when uncertain. The key innovation is providing distribution-agnostic bounds on incorrect decision probabilities, which do not rely on data dimension assumptions. Empirically, the framework improves image classifier performance in a challenging real-world task with scarce training data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI researchers have created a new way to correct mistakes made by artificial intelligence systems. This method uses “error correctors” that help an initial decision-making system make better choices. The error correctors can say either “yes, the original decision was right” or “no, don’t make a decision if you’re not sure.” This approach gives guarantees about how often it will make mistakes, and doesn’t require specific information about the data being used. In an example, this method helped improve an image classification system that was struggling to classify images with limited training data. |
Keywords
* Artificial intelligence * Image classification * Supervised
