# Learning Predictive Checklists with Probabilistic Logic Programming

arXiv: https://arxiv.org/abs/2411.16790 · HTML: https://mlsanity.com/paper/2411.16790/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 42 references that are in the map.
- First posted on arXiv 25 Nov 2024.

## Builds on (most cited first, 23 of 23)

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- [Neural Probabilistic Logic Programming in DeepProbLog](https://mlsanity.com/paper/1907.08194/) (2019)
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- [Not All Neuro-Symbolic Concepts Are Created Equal: Analysis and Mitigation of Reasoning Shortcuts](https://mlsanity.com/paper/2305.19951/) (2023)
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- [Interpretable Sequence Classification via Discrete Optimization](https://mlsanity.com/paper/2010.02819/) (2020)
- [Learning Optimal Predictive Checklists](https://mlsanity.com/paper/2112.01020/) (2021)
- [Weakly Supervised Knowledge Transfer with Probabilistic Logical Reasoning for Object Detection](https://mlsanity.com/paper/2303.05148/) (2023)
- [Learning Predictive and Interpretable Timeseries Summaries from ICU Data](https://mlsanity.com/paper/2109.11043/) (2021)
- [Learning predictive checklists from continuous medical data](https://mlsanity.com/paper/2211.07076/) (2022)
- [Revisiting Score Function Estimators for k-Subset Sampling](https://mlsanity.com/paper/2407.16058/) (2024)

## Abstract (arXiv)

Checklists have been widely recognized as effective tools for completing complex tasks in a systematic manner. Although originally intended for use in procedural tasks, their interpretability and ease of use have led to their adoption for predictive tasks as well, including in clinical settings. However, designing checklists can be challenging, often requiring expert knowledge and manual rule design based on available data. Recent work has attempted to address this issue by using machine learning to automatically generate predictive checklists from data, although these approaches have been limited to Boolean data. We propose a novel method for learning predictive checklists from diverse data modalities, such as images and time series. Our approach relies on probabilistic logic programming, a learning paradigm that enables matching the discrete nature of checklist with continuous-valued data. We propose a regularization technique to tradeoff between the information captured in discrete concepts of continuous data and permit a tunable level of interpretability for the learned checklist concepts. We demonstrate that our method outperforms various explainable machine learning techniques on prediction tasks involving image sequences, time series, and clinical notes.