Summary of Neuro-symbolic Rule Lists, by Sascha Xu et al.
Neuro-Symbolic Rule Lists
by Sascha Xu, Nils Philipp Walter, Jilles Vreeken
First submitted to arxiv on: 10 Nov 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 Machine learning models deployed in sensitive areas such as healthcare must be interpretable to ensure accountability and fairness. The paper introduces NeuRules, an end-to-end trainable model that unifies discretization, rule learning, and rule order into a single differentiable framework. This approach learns both the discretizations of individual features and their combination into conjunctive rules without any pre-processing or restrictions. In contrast to existing methods based on combinatorial optimization or neuro-symbolic methods, NeuRules consistently outperforms these approaches across a wide range of datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary NeuRules is a new way to create rule lists that can be used in situations where fairness and accountability are important, like healthcare. It’s a type of machine learning model that can understand and explain its decisions. The current methods for creating rule lists have some limitations, but NeuRules is able to overcome these challenges by unifying three important parts: discretization, rule learning, and rule order. |
Keywords
* Artificial intelligence * Machine learning * Optimization
