82 citations · 192 across the 14 of their papers we have counts for
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Feature Engineering and Forecasting via Derivative-free Optimization and Ensemble of Sequence-to-sequence Networks with Applications in Renewable Energy
Mohammad Pirhooshyaran, Katya Scheinberg, Lawrence V. Snyder
This study introduces a framework for the forecasting, reconstruction and feature engineering of multivariate processes along with its renewable energy applications. We integrate d…
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…
Sparse Inverse Covariance Selection via Alternating Linearization Methods
Katya Scheinberg, Shiqian Ma, Donald Goldfarb
Gaussian graphical models are of great interest in statistical learning. Because the conditional independencies between different nodes correspond to zero entries in the inverse co…