Summary of Verifiable Boosted Tree Ensembles, by Stefano Calzavara et al.
Verifiable Boosted Tree Ensembles
by Stefano Calzavara, Lorenzo Cazzaro, Claudio Lucchese, Giulio Ermanno Pibiri
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Logic in Computer Science (cs.LO); 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 that can be efficiently verified for robustness against attacks are crucial for many applications. This paper builds upon prior research on verifiable learning by extending the approach from basic ensemble methods to more advanced boosted tree ensembles like XGBoost and LightGBM. The results show that robustness verification can be achieved in polynomial time for attackers based on the L∞-norm, but remains NP-hard for other norm-based attackers. A pseudo-polynomial time algorithm is proposed for verifying robustness against Lp-norm attackers for any p, which offers excellent performance in practice. Experimental evaluations demonstrate that large-spread boosted ensembles are accurate enough for practical adoption while being amenable to efficient security verification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models can be very good at making predictions, but they can also make mistakes if they’re not designed correctly. This paper looks at how we can make sure machine learning models are working well and won’t make bad decisions. It builds on previous research by looking at more advanced ways to combine different predictions from many models. The results show that we can quickly check if a model is working well, but only for certain types of attacks. There’s also an algorithm that can help us check if a model is working well against other types of attacks. This could be very useful in real-world applications where security is important. |
Keywords
* Artificial intelligence * Machine learning * Xgboost
