Learning sparse two-level boolean rules
By Guolong Su, Dennis Wei, Kush R. Varshney, Dmitry M. Malioutov. 2016
31 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Molecular Biology
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
- Marco F. Huber
- Nadia Burkart
- Q. Liao
- Dennis Wei
- Ronny Luss
- Karthikeyan Shanmugam
- Amit Dhurandhar
- Sanjeeb Dash
In Molecular Biology
- Nadia Burkart
- Marco F. Huber
- Q. Liao
- Pin-Yu Chen
- Sanjeeb Dash
- Kush R. Varshney
- Oktay Günlük
- Dennis Wei
Citations in the corpus
- Boolean Decision Rules via Column Generation (2018, methodology): “For these experiments, we used the second comparison method, as also recommended in [52, 46], with sample deciles as thresholds.”
- A Survey on the Explainability of Supervised Machine Learning (2020, methodology): “Two-Level Boolean Rules (TLBR) create classification predictions by connecting features with logical statements in rules (Su et al., 2016).”
- One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques (2019, background): “[58], and the alternating minimization (AM) and block coordinate descent (BCD) algorithms from [54].”