12 citations · 13 across the 2 of their papers we have counts for
3 papers
stat.ML2019★ 1 cited
Novel and Efficient Approximations for Zero-One Loss of Linear Classifiers
Hiva Ghanbari, Minhan Li, Katya Scheinberg
The predictive quality of machine learning models is typically measured in terms of their (approximate) expected prediction accuracy or the so-called Area Under the Curve (AUC). Mi…
cs.LG2018
Directly and Efficiently Optimizing Prediction Error and AUC of Linear Classifiers
Hiva Ghanbari, Katya Scheinberg
The predictive quality of machine learning models is typically measured in terms of their (approximate) expected prediction error or the so-called Area Under the Curve (AUC) for a…
cs.LG2017★ 12 cited
Black-Box Optimization in Machine Learning with Trust Region Based Derivative Free Algorithm
Hiva Ghanbari, Katya Scheinberg
In this work, we utilize a Trust Region based Derivative Free Optimization (DFO-TR) method to directly maximize the Area Under Receiver Operating Characteristic Curve (AUC), which…