Summary of Colep: Certifiably Robust Learning-reasoning Conformal Prediction Via Probabilistic Circuits, by Mintong Kang et al.
COLEP: Certifiably Robust Learning-Reasoning Conformal Prediction via Probabilistic Circuits
by Mintong Kang, Nezihe Merve Gürel, Linyi Li, Bo Li
First submitted to arxiv on: 17 Mar 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 The proposed certifiably robust learning-reasoning conformal prediction framework (COLEP) via probabilistic circuits combines data-driven learning with logic reasoning to construct statistically rigorous prediction sets for arbitrary black-box machine learning models. By employing probabilistic circuits within the reasoning component, COLEP achieves exact and efficient reasoning while providing end-to-end certification of prediction coverage in the presence of bounded adversarial perturbations. The framework also considers the finite size of the calibration set, ensuring certified coverage is maintained. Empirical results demonstrate up to 12% improvement in certified coverage on various datasets, including GTSRB, CIFAR10, and AwA2. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary COLEP is a new way to make predictions that are both accurate and reliable. It works by combining two parts: learning and reasoning. The learning part uses data to train models that understand different concepts. The reasoning part uses these models to figure out how they relate to each other, making it possible to reason about the predictions. This approach is special because it provides guarantees about how good its predictions are, even when there’s some noise or “adversarial” behavior in the data. This means COLEP can be trusted to make accurate and reliable predictions. |
Keywords
* Artificial intelligence * Machine learning
