Katya Scheinberg | DeepAI

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∙ 06/21/2022

Finding Optimal Policy for Queueing Models: New Parameterization

Queueing systems appear in many important real-life applications includi...

0 Trang H. Tran, et al. ∙

∙ 02/07/2022

Nesterov Accelerated Shuffling Gradient Method for Convex Optimization

In this paper, we propose Nesterov Accelerated Shuffling Gradient (NASG)...

0 Trang H. Tran, et al. ∙

∙ 01/18/2020

Adaptive Stochastic Optimization

Optimization lies at the heart of machine learning and signal processing...

0 Frank E. Curtis, et al. ∙

∙ 09/24/2019

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...

0 Kostas Hatalis, et al. ∙

∙ 09/12/2019

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...

0 Mohammad Pirhooshyaran, et al. ∙

∙ 05/29/2019

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

In this paper, we consider derivative free optimization problems, where ...

0 Albert S. Berahas, et al. ∙

∙ 02/28/2019

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

The predictive quality of machine learning models is typically measured ...

0 Hiva Ghanbari, et al. ∙

∙ 11/25/2018

Inexact SARAH Algorithm for Stochastic Optimization

We develop and analyze a variant of variance reducing stochastic gradien...

0 Lam M. Nguyen, et al. ∙

∙ 03/29/2018

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...

0 Kostas Hatalis, et al. ∙

∙ 02/11/2018

SGD and Hogwild! Convergence Without the Bounded Gradients Assumption

Stochastic gradient descent (SGD) is the optimization algorithm of choic...

0 Lam M. Nguyen, et al. ∙

∙ 02/07/2018

Directly and Efficiently Optimizing Prediction Error and AUC of Linear Classifiers

The predictive quality of machine learning models is typically measured ...

0 Hiva Ghanbari, et al. ∙

∙ 01/18/2018

When Does Stochastic Gradient Algorithm Work Well?

In this paper, we consider a general stochastic optimization problem whi...

0 Lam M. Nguyen, et al. ∙

∙ 12/29/2017

A Stochastic Trust Region Algorithm

An algorithm is proposed for solving stochastic and finite sum minimizat...

0 Frank E. Curtis, et al. ∙

∙ 10/04/2017

Smooth Pinball Neural Network for Probabilistic Forecasting of Wind Power

Uncertainty analysis in the form of probabilistic forecasting can signif...

0 Kostas Hatalis, et al. ∙

∙ 06/30/2017

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...

0 Frank E. Curtis, et al. ∙

∙ 05/20/2017

Stochastic Recursive Gradient Algorithm for Nonconvex Optimization

In this paper, we study and analyze the mini-batch version of StochAstic...

0 Lam M. Nguyen, et al. ∙

∙ 03/01/2017

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

In this paper, we propose a StochAstic Recursive grAdient algoritHm (SAR...

0 Lam M. Nguyen, et al. ∙

∙ 07/11/2016

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...

0 Hiva Ghanbari, et al. ∙

∙ 11/26/2013

Practical Inexact Proximal Quasi-Newton Method with Global Complexity Analysis

Recently several methods were proposed for sparse optimization which mak...

0 Katya Scheinberg, et al. ∙

∙ 03/27/2013

Efficiently Using Second Order Information in Large l1 Regularization Problems

We propose a novel general algorithm LHAC that efficiently uses second-o...

0 Xiaocheng Tang, et al. ∙

∙ 10/30/2010

Sparse Inverse Covariance Selection via Alternating Linearization Methods

Gaussian graphical models are of great interest in statistical learning....

0 Katya Scheinberg, et al. ∙

∙ 12/23/2009

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

We present in this paper first-order alternating linearization algorithm...

0 Donald Goldfarb, et al. ∙

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