12 citations · 14 across the 3 of their papers we have counts for
4 papers
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…
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…
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…
Proximal Quasi-Newton Methods for Regularized Convex Optimization with Linear and Accelerated Sublinear Convergence Rates
Hiva Ghanbari, Katya Scheinberg
In [19], a general, inexact, efficient proximal quasi-Newton algorithm for composite optimization problems has been proposed and a sublinear global convergence rate has been establ…