Summary of Probabilistic Circuits with Constraints Via Convex Optimization, by Soroush Ghandi et al.
Probabilistic Circuits with Constraints via Convex Optimization
by Soroush Ghandi, Benjamin Quost, Cassio de Campos
First submitted to arxiv on: 19 Mar 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 A novel approach integrates probabilistic propositional logic constraints with probabilistic circuits (PCs) to produce a new PC that satisfies the constraints, enabling efficient computation of conditional and marginal probabilities. This method leverages convex optimization without retraining the entire model, achieving state-of-the-art performance in some domains. Empirical evaluations demonstrate multiple use cases, including improving model performance under data scarcity or incompleteness, as well as enforcing machine learning fairness measures without compromising model fitness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research combines logic and deep probabilistic models to create new possibilities for applications. By integrating constraints into PCs, the approach can be used to improve model performance with limited data and enforce fairness measures without sacrificing model accuracy. |
Keywords
* Artificial intelligence * Machine learning * Optimization
