Summary of Conformal Predictions For Probabilistically Robust Scalable Machine Learning Classification, by Alberto Carlevaro et al.
Conformal Predictions for Probabilistically Robust Scalable Machine Learning Classification
by Alberto Carlevaro, Teodoro Alamo Cantarero, Fabrizio Dabbene, Maurizio Mongelli
First submitted to arxiv on: 15 Mar 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
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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 explores the intersection of conformal predictions and scalable classifiers, introducing a new score function and “conformal safety set” that enables reliable classification from the outset. By linking classical classifiers to statistical order theory and probabilistic learning theory, the authors generalize the concept of scalable classifier, demonstrating its practical implications in cybersecurity for identifying DNS tunneling attacks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making machine learning more reliable and safe. It introduces a new way to evaluate if an algorithm is good enough to use, by defining “scalable classifiers” that can be trusted from the start. The authors show how this works in practice by using it to identify cyber attacks on computer networks. |
Keywords
* Artificial intelligence * Classification * Machine learning
