# I Know Therefore I Score: Label-Free Crafting of Scoring Functions using Constraints Based on Domain Expertise

arXiv: https://arxiv.org/abs/2203.10085 · HTML: https://mlsanity.com/paper/2203.10085/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 29 references that are in the map.
- First posted on arXiv 18 Mar 2022.

## Builds on (most cited first, 9 of 9)

- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations](https://mlsanity.com/paper/1711.10561/) (2017)
- [Label-Free Supervision of Neural Networks with Physics and Domain Knowledge](https://mlsanity.com/paper/1609.05566/) (2016)
- [Neural Ranking Models with Weak Supervision](https://mlsanity.com/paper/1704.08803/) (2017)
- [Explainable AI for Interpretable Credit Scoring](https://mlsanity.com/paper/2012.03749/) (2020)
- [Injecting Domain Knowledge in Neural Networks: a Controlled Experiment on a Constrained Problem](https://mlsanity.com/paper/2002.10742/) (2020)
- [Passage Ranking with Weak Supervision](https://mlsanity.com/paper/1905.05910/) (2019)

## Abstract (arXiv)

Several real-life applications require crafting concise, quantitative scoring functions (also called rating systems) from measured observations. For example, an effectiveness score needs to be created for advertising campaigns using a number of engagement metrics. Experts often need to create such scoring functions in the absence of labelled data, where the scores need to reflect business insights and rules as understood by the domain experts. Without a way to capture these inputs systematically, this becomes a time-consuming process involving trial and error. In this paper, we introduce a label-free practical approach to learn a scoring function from multi-dimensional numerical data. The approach incorporates insights and business rules from domain experts in the form of easily observable and specifiable constraints, which are used as weak supervision by a machine learning model. We convert such constraints into loss functions that are optimized simultaneously while learning the scoring function. We examine the efficacy of the approach using a synthetic dataset as well as four real-life datasets, and also compare how it performs vis-a-vis supervised learning models.