A Semiquantitative Group Testing Approach for Learning Interpretable Clinical Prediction Rules

By Amin Emad, Kush R. Varshney, Dmitry M. Malioutov. 2015

How researchers see this paper

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

1. Kush R. Varshney
2. Kuldeep S. Meel
3. Dmitry M. Malioutov
4. Cynthia Rudin
5. Tong Wang
6. Sanjeeb Dash
7. Bishwamittra Ghosh
8. Guolong Su

Citations in the corpus

• MLIC: A MaxSAT-Based Framework for Learning Interpretable Classification Rules (2018, background): “LP relaxations have been considered for learning scorecards from data [19], and our MaxSAT-based framework can be directly extended.”
• Data Science of the People , for the People , by the People : A Viewpoint on an Emerging Dichotomy (2015, background): “However, recent work is revisiting the problem of interpretable learning through principled optimization formulations and achieving predictive performance approaching that of uninterpretable methods [40, 24, 18, 10, 34, 30, 55, 33, 50, 20].”
• Group testing: an information theory perspective (2019, unknown): “In [70], [142], the authors show how such rules can be obtained using a relaxed noisy linear programming formulation of group testing (to be introduced in Section 3.”