Summary of Learning Model Agnostic Explanations Via Constraint Programming, by Frederic Koriche et al.
Learning Model Agnostic Explanations via Constraint Programming
by Frederic Koriche, Jean-Marie Lagniez, Stefan Mengel, Chi Tran
First submitted to arxiv on: 13 Nov 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 The paper proposes a novel framework for interpretable machine learning, focusing on explaining predictions made by opaque classifiers like ensemble models, kernel methods, or neural networks. By framing the task as a constraint optimization problem, the approach seeks to identify a small set of features that jointly determine the black box response with minimal error. This constraint programming approach offers theoretical guarantees and is evaluated empirically on various datasets, outperforming the state-of-the-art Anchors method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how machines make decisions by figuring out what’s important for them to make those predictions. It uses a special problem-solving technique called constraint optimization to find answers that are accurate but not too complicated. The approach is tested on different datasets and does better than the current best method. |
Keywords
» Artificial intelligence » Machine learning » Optimization
