A Provable Algorithm for Learning Interpretable Scoring Systems

By Nataliya Sokolovska, Yann Chevaleyre, Jean-Daniel Zucker. 2018

21 citations recorded by the source corpus.

Subfields: Safety, Robustness & Interpretability, Statistical Learning Theory

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Safety, Robustness & Interpretability

1. Lesia Semenova
2. Cynthia Rudin
3. Berk Ustun
4. Zhi Chen
5. Chudi Zhong
6. Haiyang Huang
7. Tong Wang
8. Octavio Loyola-González

In Statistical Learning Theory

1. Berk Ustun
2. Chudi Zhong
3. Cynthia Rudin
4. Zhi Chen
5. Haiyang Huang
6. Lesia Semenova
7. Tong Wang
8. Ş. Ertekin

Citations in the corpus

• Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, background): “Several works solve approximate versions of (3), including works of Sokolovska et al. (2018, 2017); Billiet et al. (2018, 2017); Carrizosa et al. (2016).”
• Learning Optimized Risk Scores (2016, methodology): “…scoring systems for decision-making (see, e.g., Ustun and Rudin, 2016; Zeng et al., 2016; Carrizosa et al., 2016; Van Belle et al., 2013; Billiet et al., 2016, 2017; Sokolovska et al., 2017, 2018), the method in this work aims to produce scoring systems for risk assessment (i.e., risk scores).”
• Actionable Recourse in Linear Classification (2018, background): “1While transparency does not ensure recourse, simple models such as scoring systems and rule-based models [3, 28, 42, 49, 50] have the benefit that they allow end-users to check the feasibility of recourse without a computer.”