# Pruning-Based Extraction of Descriptions from Probabilistic Circuits

arXiv: https://arxiv.org/abs/2311.13379 · HTML: https://mlsanity.com/paper/2311.13379/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 48 references that are in the map.
- First posted on arXiv 22 Nov 2023.

## Builds on (most cited first, 17 of 17)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Interpretable machine learning: definitions, methods, and applications](https://mlsanity.com/paper/1901.04592/) (2019)
- [Learning from positive and unlabeled data: a survey](https://mlsanity.com/paper/1811.04820/) (2018)
- [A Symbolic Approach to Explaining Bayesian Network Classifiers](https://mlsanity.com/paper/1805.03364/) (2018)
- [A Differential Approach to Inference in Bayesian Networks](https://mlsanity.com/paper/1301.3847/) (2013)
- [Tractable Regularization of Probabilistic Circuits](https://mlsanity.com/paper/2106.02264/) (2021)
- [Probabilistic Sufficient Explanations](https://mlsanity.com/paper/2105.10118/) (2021)
- [Sparse Probabilistic Circuits via Pruning and Growing](https://mlsanity.com/paper/2211.12551/) (2022)
- [Versatile Verification of Tree Ensembles](https://mlsanity.com/paper/2010.13880/) (2020)
- [Probabilistic Circuits That Know What They Don't Know](https://mlsanity.com/paper/2302.06544/) (2023)
- [Automatic Music Playlist Generation via Simulation-based Reinforcement Learning](https://mlsanity.com/paper/2310.09123/) (2023)
- [Towards Playlist Generation Algorithms Using RNNs Trained on Within-Track Transitions](https://mlsanity.com/paper/1606.02096/) (2016)
- [Explainable Models via Compression of Tree Ensembles](https://mlsanity.com/paper/2206.07904/) (2022)
- [Certifying Fairness of Probabilistic Circuits](https://mlsanity.com/paper/2212.02474/) (2022)
- [Automatic Generation of Product Concepts from Positive Examples, with an Application to Music Streaming](https://mlsanity.com/paper/2210.01515/) (2022)

## Abstract (arXiv)

Concept learning is a general task with applications in various domains. As a motivating example we consider the application of music playlist generation, where a playlist is represented as a concept (e.g., `relaxing music') rather than as a fixed collection of songs. In this work we use a probabilistic circuit to learn a concept from positively labelled and unlabelled examples. While these circuits form an attractive tractable model for this task, it is challenging for a domain expert to inspect and analyse them, which impedes their use within certain applications. We propose to resolve this by converting a learned probabilistic circuit into a logic-based discriminative model that covers the high density regions of the circuit. That is, those regions the circuit classifies as certainly being part of the learned concept. As part of this approach we present two contributions: PUTPUT, an algorithm to prune low density regions from a probabilistic circuit while considering both the F1-score and a newly proposed description length that we call aggregated entropy. Our experiments demonstrate the effectiveness of our approach in providing discriminative models, outperforming competitors on the music playlist generation task and similar datasets.