99 citations · 600 across the 38 of their papers we have counts for
8 papers · 1 filter
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…
An Empirical Analysis of Constrained Support Vector Quantile Regression for Nonparametric Probabilistic Forecasting of Wind Power
Kostas Hatalis, Shalinee Kishore, Katya Scheinberg +1
Uncertainty analysis in the form of probabilistic forecasting can provide significant improvements in decision-making processes in the smart power grid for better integrating renew…
When Does Stochastic Gradient Algorithm Work Well?
Lam M. Nguyen, Nam H. Nguyen, Dzung T. Phan +2
In this paper, we consider a general stochastic optimization problem which is often at the core of supervised learning, such as deep learning and linear classification. We consider…
Smooth Pinball Neural Network for Probabilistic Forecasting of Wind Power
Kostas Hatalis, Alberto J. Lamadrid, Katya Scheinberg +1
Uncertainty analysis in the form of probabilistic forecasting can significantly improve decision making processes in the smart power grid for better integrating renewable energy so…
Optimization Methods for Supervised Machine Learning: From Linear Models to Deep Learning
Frank E. Curtis, Katya Scheinberg
The goal of this tutorial is to introduce key models, algorithms, and open questions related to the use of optimization methods for solving problems arising in machine learning. It…
Stochastic Recursive Gradient Algorithm for Nonconvex Optimization
Lam M. Nguyen, Jie Liu, Katya Scheinberg +1
In this paper, we study and analyze the mini-batch version of StochAstic Recursive grAdient algoritHm (SARAH), a method employing the stochastic recursive gradient, for solving emp…