Katya Scheinberg

research

∙ 06/21/2022

Finding Optimal Policy for Queueing Models: New Parameterization

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

Trang H. Tran, et al. ∙

research

∙ 02/07/2022

Nesterov Accelerated Shuffling Gradient Method for Convex Optimization

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

Trang H. Tran, et al. ∙

research

∙ 01/18/2020

Adaptive Stochastic Optimization

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

Frank E. Curtis, et al. ∙

research

∙ 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...

Kostas Hatalis, et al. ∙

research

∙ 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...

Mohammad Pirhooshyaran, et al. ∙

research

∙ 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 ...

Albert S. Berahas, et al. ∙

research

∙ 02/28/2019

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

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

Hiva Ghanbari, et al. ∙

research

∙ 11/25/2018

Inexact SARAH Algorithm for Stochastic Optimization

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

Lam M. Nguyen, et al. ∙

research

∙ 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...

Kostas Hatalis, et al. ∙

research

∙ 02/11/2018

SGD and Hogwild! Convergence Without the Bounded Gradients Assumption

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

Lam M. Nguyen, et al. ∙

research

∙ 02/07/2018

Directly and Efficiently Optimizing Prediction Error and AUC of Linear Classifiers

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

Hiva Ghanbari, et al. ∙

research

∙ 01/18/2018

When Does Stochastic Gradient Algorithm Work Well?

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

Lam M. Nguyen, et al. ∙

research

∙ 12/29/2017

A Stochastic Trust Region Algorithm

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

Frank E. Curtis, et al. ∙

research

∙ 10/04/2017

Smooth Pinball Neural Network for Probabilistic Forecasting of Wind Power

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

Kostas Hatalis, et al. ∙

research

∙ 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...

Frank E. Curtis, et al. ∙

research

∙ 05/20/2017

Stochastic Recursive Gradient Algorithm for Nonconvex Optimization

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

Lam M. Nguyen, et al. ∙

research

∙ 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...

Lam M. Nguyen, et al. ∙

research

∙ 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...

Hiva Ghanbari, et al. ∙

research

∙ 11/26/2013

Practical Inexact Proximal Quasi-Newton Method with Global Complexity Analysis

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

Katya Scheinberg, et al. ∙

research

∙ 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...

Xiaocheng Tang, et al. ∙

research

∙ 10/30/2010

Sparse Inverse Covariance Selection via Alternating Linearization Methods

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

Katya Scheinberg, et al. ∙

research

∙ 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...

Donald Goldfarb, et al. ∙

Katya Scheinberg

Featured Co-authors

Peter Richtarik

Jie Liu

Krzysztof Choromanski

Martin Takáč

Lam M. Nguyen

Shiqian Ma

Marten van Dijk

Donald Goldfarb

Phuong Ha Nguyen

Xiaocheng Tang

Dzung T. Phan

Finding Optimal Policy for Queueing Models: New Parameterization

Nesterov Accelerated Shuffling Gradient Method for Convex Optimization

Adaptive Stochastic Optimization

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

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

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

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

Inexact SARAH Algorithm for Stochastic Optimization

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

SGD and Hogwild! Convergence Without the Bounded Gradients Assumption

Directly and Efficiently Optimizing Prediction Error and AUC of Linear Classifiers

When Does Stochastic Gradient Algorithm Work Well?

A Stochastic Trust Region Algorithm

Smooth Pinball Neural Network for Probabilistic Forecasting of Wind Power

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

Stochastic Recursive Gradient Algorithm for Nonconvex Optimization

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

Proximal Quasi-Newton Methods for Regularized Convex Optimization with Linear and Accelerated Sublinear Convergence Rates

Practical Inexact Proximal Quasi-Newton Method with Global Complexity Analysis

Efficiently Using Second Order Information in Large l1 Regularization Problems

Sparse Inverse Covariance Selection via Alternating Linearization Methods

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