Katya Scheinberg

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Lehigh University

Harvey E. Wagner Endowed Chair Professor at the Industrial and Systems Engineering Department at Lehigh University

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Adaptive Stochastic Optimization

Optimization lies at the heart of machine learning and signal processing...
01/18/2020 ∙ by Frank E. Curtis, et al. ∙ 0 ∙ share

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A Novel Smoothed Loss and Penalty Function for Noncrossing Composite Quantile Estimation via Deep Neural Networks

Uncertainty analysis in the form of probabilistic forecasting can signif...
09/24/2019 ∙ by Kostas Hatalis, et al. ∙ 0 ∙ share

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Feature Engineering and Forecasting via Integration of Derivative-free Optimization and Ensemble of Sequence-to-sequence Networks: Renewable Energy Case Studies

This research introduces a framework for forecasting, reconstruction and...
09/12/2019 ∙ by Mohammad Pirhooshyaran, et al. ∙ 0 ∙ share

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Linear interpolation gives better gradients than Gaussian smoothing in derivative-free optimization

In this paper, we consider derivative free optimization problems, where ...
05/29/2019 ∙ by Albert S. Berahas, et al. ∙ 0 ∙ share

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Novel and Efficient Approximations for Zero-One Loss of Linear Classifiers

The predictive quality of machine learning models is typically measured ...
02/28/2019 ∙ by Hiva Ghanbari, et al. ∙ 0 ∙ share

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Inexact SARAH Algorithm for Stochastic Optimization

We develop and analyze a variant of variance reducing stochastic gradien...
11/25/2018 ∙ by Lam M. Nguyen, et al. ∙ 0 ∙ share

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An Empirical Analysis of Constrained Support Vector Quantile Regression for Nonparametric Probabilistic Forecasting of Wind Power

Uncertainty analysis in the form of probabilistic forecasting can provid...
03/29/2018 ∙ by Kostas Hatalis, et al. ∙ 0 ∙ share

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SGD and Hogwild! Convergence Without the Bounded Gradients Assumption

Stochastic gradient descent (SGD) is the optimization algorithm of choic...
02/11/2018 ∙ by Lam M. Nguyen, et al. ∙ 0 ∙ share

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Directly and Efficiently Optimizing Prediction Error and AUC of Linear Classifiers

The predictive quality of machine learning models is typically measured ...
02/07/2018 ∙ by Hiva Ghanbari, et al. ∙ 0 ∙ share

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When Does Stochastic Gradient Algorithm Work Well?

In this paper, we consider a general stochastic optimization problem whi...
01/18/2018 ∙ by Lam M. Nguyen, et al. ∙ 0 ∙ share

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A Stochastic Trust Region Algorithm

An algorithm is proposed for solving stochastic and finite sum minimizat...
12/29/2017 ∙ by Frank E. Curtis, et al. ∙ 0 ∙ share

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Smooth Pinball Neural Network for Probabilistic Forecasting of Wind Power

Uncertainty analysis in the form of probabilistic forecasting can signif...
10/04/2017 ∙ by Kostas Hatalis, et al. ∙ 0 ∙ share

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Optimization Methods for Supervised Machine Learning: From Linear Models to Deep Learning

The goal of this tutorial is to introduce key models, algorithms, and op...
06/30/2017 ∙ by Frank E. Curtis, et al. ∙ 0 ∙ share

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Stochastic Recursive Gradient Algorithm for Nonconvex Optimization

In this paper, we study and analyze the mini-batch version of StochAstic...
05/20/2017 ∙ by Lam M. Nguyen, et al. ∙ 0 ∙ share

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SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient

In this paper, we propose a StochAstic Recursive grAdient algoritHm (SAR...
03/01/2017 ∙ by Lam M. Nguyen, et al. ∙ 0 ∙ share

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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 f...
07/11/2016 ∙ by Hiva Ghanbari, et al. ∙ 0 ∙ share

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Practical Inexact Proximal Quasi-Newton Method with Global Complexity Analysis

Recently several methods were proposed for sparse optimization which mak...
11/26/2013 ∙ by Katya Scheinberg, et al. ∙ 0 ∙ share

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Efficiently Using Second Order Information in Large l1 Regularization Problems

We propose a novel general algorithm LHAC that efficiently uses second-o...
03/27/2013 ∙ by Xiaocheng Tang, et al. ∙ 0 ∙ share

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Sparse Inverse Covariance Selection via Alternating Linearization Methods

Gaussian graphical models are of great interest in statistical learning....
10/30/2010 ∙ by Katya Scheinberg, et al. ∙ 0 ∙ share

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Fast Alternating Linearization Methods for Minimizing the Sum of Two Convex Functions

We present in this paper first-order alternating linearization algorithm...
12/23/2009 ∙ by Donald Goldfarb, et al. ∙ 0 ∙ share

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