no code implementations • 19 Mar 2021 • Tian Huang, Siong Thye Goh, Sabrish Gopalakrishnan, Tao Luo, Qianxiao Li, Hoong Chuin Lau
In this way, we are able capture the common structure of the instances and their interactions with the solver, and produce good choices of penalty parameters with fewer number of calls to the QUBO solver.
1 code implementation • 10 Mar 2018 • Siong Thye Goh, Cynthia Rudin
We present a new machine learning approach to estimate personalized treatment effects in the classical potential outcomes framework with binary outcomes.
1 code implementation • 22 Oct 2015 • Siong Thye Goh, Lesia Semenova, Cynthia Rudin
We present sparse tree-based and list-based density estimation methods for binary/categorical data.
1 code implementation • 13 Mar 2014 • Siong Thye Goh, Cynthia Rudin
The vast majority of real world classification problems are imbalanced, meaning there are far fewer data from the class of interest (the positive class) than from other classes.