research
∙
06/21/2022
Finding Optimal Policy for Queueing Models: New Parameterization
Queueing systems appear in many important real-life applications includi...
share
research
∙
02/07/2022
Nesterov Accelerated Shuffling Gradient Method for Convex Optimization
In this paper, we propose Nesterov Accelerated Shuffling Gradient (NASG)...
research
∙
01/18/2020
Adaptive Stochastic Optimization
Optimization lies at the heart of machine learning and signal processing...
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...
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...
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 ...
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 ...
research
∙
11/25/2018
Inexact SARAH Algorithm for Stochastic Optimization
We develop and analyze a variant of variance reducing stochastic gradien...
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...
research
∙
02/11/2018
SGD and Hogwild! Convergence Without the Bounded Gradients Assumption
Stochastic gradient descent (SGD) is the optimization algorithm of choic...
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 ...
research
∙
01/18/2018
When Does Stochastic Gradient Algorithm Work Well?
In this paper, we consider a general stochastic optimization problem whi...
research
∙
12/29/2017
A Stochastic Trust Region Algorithm
An algorithm is proposed for solving stochastic and finite sum minimizat...
research
∙
10/04/2017
Smooth Pinball Neural Network for Probabilistic Forecasting of Wind Power
Uncertainty analysis in the form of probabilistic forecasting can signif...
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...
research
∙
05/20/2017
Stochastic Recursive Gradient Algorithm for Nonconvex Optimization
In this paper, we study and analyze the mini-batch version of StochAstic...
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...
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...
research
∙
11/26/2013
Practical Inexact Proximal Quasi-Newton Method with Global Complexity Analysis
Recently several methods were proposed for sparse optimization which mak...
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...
research
∙
10/30/2010
Sparse Inverse Covariance Selection via Alternating Linearization Methods
Gaussian graphical models are of great interest in statistical learning....
research
∙
12/23/2009