
Adaptive Stochastic Optimization
Optimization lies at the heart of machine learning and signal processing...
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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...
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Feature Engineering and Forecasting via Integration of Derivativefree Optimization and Ensemble of Sequencetosequence Networks: Renewable Energy Case Studies
This research introduces a framework for forecasting, reconstruction and...
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Linear interpolation gives better gradients than Gaussian smoothing in derivativefree optimization
In this paper, we consider derivative free optimization problems, where ...
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Novel and Efficient Approximations for ZeroOne Loss of Linear Classifiers
The predictive quality of machine learning models is typically measured ...
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Inexact SARAH Algorithm for Stochastic Optimization
We develop and analyze a variant of variance reducing stochastic gradien...
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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...
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SGD and Hogwild! Convergence Without the Bounded Gradients Assumption
Stochastic gradient descent (SGD) is the optimization algorithm of choic...
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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 ...
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When Does Stochastic Gradient Algorithm Work Well?
In this paper, we consider a general stochastic optimization problem whi...
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A Stochastic Trust Region Algorithm
An algorithm is proposed for solving stochastic and finite sum minimizat...
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Smooth Pinball Neural Network for Probabilistic Forecasting of Wind Power
Uncertainty analysis in the form of probabilistic forecasting can signif...
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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...
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Stochastic Recursive Gradient Algorithm for Nonconvex Optimization
In this paper, we study and analyze the minibatch version of StochAstic...
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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...
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Proximal QuasiNewton Methods for Regularized Convex Optimization with Linear and Accelerated Sublinear Convergence Rates
In [19], a general, inexact, efficient proximal quasiNewton algorithm f...
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Practical Inexact Proximal QuasiNewton Method with Global Complexity Analysis
Recently several methods were proposed for sparse optimization which mak...
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Efficiently Using Second Order Information in Large l1 Regularization Problems
We propose a novel general algorithm LHAC that efficiently uses secondo...
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Sparse Inverse Covariance Selection via Alternating Linearization Methods
Gaussian graphical models are of great interest in statistical learning....
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Fast Alternating Linearization Methods for Minimizing the Sum of Two Convex Functions
We present in this paper firstorder alternating linearization algorithm...
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Katya Scheinberg
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Harvey E. Wagner Endowed Chair Professor at the Industrial and Systems Engineering Department at Lehigh University