The fused lasso penalty for learning interpretable medical scoring systems

By Nataliya Sokolovska, Yann Chevaleyre, Karine Clément, Jean-Daniel Zucker. 2017

10 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability, Optimization & Training, Medical & Health AI

How researchers see this paper

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

In Artificial Intelligence

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

In Statistics and Probability

1. Cynthia Rudin
2. Berk Ustun
3. Lesia Semenova
4. Chudi Zhong
5. Zhi Chen
6. Haiyang Huang
7. Nadia Burkart
8. Marco F. Huber

In Optimization & Training

1. Lesia Semenova
2. Chudi Zhong
3. Zhi Chen
4. Haiyang Huang
5. Berk Ustun
6. Cynthia Rudin

In Medical & Health AI

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

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): “These include methods to learn: scoring systems for decision-making (Carrizosa et al., 2016; Van Belle et al., 2013; Billiet et al., 2016, 2017; Sokolovska et al., 2017); sparse Boolean classifiers (e.g., Chevaleyre et al., 2013; Malioutov and Varshney, 2013; Goh and Rudin, 2014; Wang et al.,…”