Summary of Efficient Contrastive Explanations on Demand, by Yacine Izza and Joao Marques-silva
Efficient Contrastive Explanations on Demand
by Yacine Izza, Joao Marques-Silva
First submitted to arxiv on: 24 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 recent connection between adversarial robustness and symbolic explanations has significant implications for making computation as efficient as deciding adversarial examples, especially for complex ML models. This paper proposes novel algorithms to compute contrastive explanations for ML models with many features, leveraging on adversarial robustness. The approach includes novel algorithms for listing and finding smallest contrastive explanations, which achieve performance gains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research connects adversarial robustness and symbolic explanations in machine learning. It’s a big deal because it could make explaining complex AI models easier and faster. To do this, the researchers came up with new ways to calculate “contrastive explanations” for models with lots of features. They also found ways to list and find the smallest explanations that work best. The results show that their new methods can improve performance. |
Keywords
» Artificial intelligence » Machine learning
