Add to ORKGIn the first chapter of this thesis, we analyze the global convergence rate of a proximal quasi-Newton algorithm for solving composite optimization problems, in both exact and inexact settings, in the case when the objective function is strongly convex.
University of Minnesota Ph.D. dissertation. September 2017. Major: Electrical/Computer Engineering. ...
Optimization is an important discipline of applied mathematics with far-reaching applications. Optim...
In this thesis, we propose efficient algorithms and provide theoretical analysis through the angle o...
The scale of modern datasets necessitates the development of efficient distributed opti- mization me...
The dissertation addresses the research topics of machine learning outlined below. We developed the ...
This thesis proposes several optimization methods that utilize parallel algorithms for large-scale m...
In this thesis, we focus on Frank-Wolfe (a.k.a. Conditional Gradient) algorithms, a family of iterat...
Machine learning has become one of the most exciting research areas in the world, with various appli...
Machine learning has been a topic in academia and industry for decades. Performance of machine lear...
In this thesis we explore different mathematical techniques for extracting information from data. In...
Many fundamental machine learning tasks can be formulated as min-max optimization. This motivates us...
Nonconvex optimization naturally arises in many machine learning problems. Machine learning research...
International audienceA new result in convex analysis on the calculation of proximity operators in c...
The interplay between optimization and machine learning is one of the most important developments in...
International audienceWe propose an inexact variable-metric proximal point algorithm to accelerate g...
University of Minnesota Ph.D. dissertation. September 2017. Major: Electrical/Computer Engineering. ...
Optimization is an important discipline of applied mathematics with far-reaching applications. Optim...
In this thesis, we propose efficient algorithms and provide theoretical analysis through the angle o...
The scale of modern datasets necessitates the development of efficient distributed opti- mization me...
The dissertation addresses the research topics of machine learning outlined below. We developed the ...
This thesis proposes several optimization methods that utilize parallel algorithms for large-scale m...
In this thesis, we focus on Frank-Wolfe (a.k.a. Conditional Gradient) algorithms, a family of iterat...
Machine learning has become one of the most exciting research areas in the world, with various appli...
Machine learning has been a topic in academia and industry for decades. Performance of machine lear...
In this thesis we explore different mathematical techniques for extracting information from data. In...
Many fundamental machine learning tasks can be formulated as min-max optimization. This motivates us...
Nonconvex optimization naturally arises in many machine learning problems. Machine learning research...
International audienceA new result in convex analysis on the calculation of proximity operators in c...
The interplay between optimization and machine learning is one of the most important developments in...
International audienceWe propose an inexact variable-metric proximal point algorithm to accelerate g...
University of Minnesota Ph.D. dissertation. September 2017. Major: Electrical/Computer Engineering. ...
Optimization is an important discipline of applied mathematics with far-reaching applications. Optim...
In this thesis, we propose efficient algorithms and provide theoretical analysis through the angle o...