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Novel and Efficient Approximations for Zero-One Loss of Linear Classifiers
In this work, we show that in the case of linear predictors, the expected error and the expected ranking loss can be effectively approximated by smooth functions whose closed form expressions and those of their first (and second) order derivatives depend on the first and second moments of the data distribution, which can be precomputed.
Directly and Efficiently Optimizing Prediction Error and AUC of Linear Classifiers
We show that even when data is not normally distributed, computed derivatives are sufficiently useful to render an efficient optimization method and high quality solutions.
Black-Box Optimization in Machine Learning with Trust Region Based Derivative Free Algorithm
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 is a nonsmooth, noisy function.
Proximal Quasi-Newton Methods for Regularized Convex Optimization with Linear and Accelerated Sublinear Convergence Rates
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 established.
