OML 2018: Optimization for Machine Learning and Machine Learning for Optimization |
Submission link | https://easychair.org/conferences/?conf=oml2018 |
Abstract registration deadline | March 15, 2018 |
Submission deadline | June 30, 2018 |
Call for Book Chapter
Open Science
Open Access Journal with DOI (Indexed)
Title of the book
Optimization for Machine Learning
and Machine Learning for Optimization
Subject of the book
Machine learning is revolutionizing our world. It is difficult to imagine another information technology that has progressed so swiftly in recent years, in terms of real impact.
The fields of machine learning and optimization are highly interwoven. Optimization problems forms the core of machine learning methods and modern optimization algorithms are increasingly using machine learning to improve their efficiency. This book examines the interplay between those two fields, highlighting their core similarities and how they may differ from each other on common mathematical problems.
Machine learning finds its applications in all areas of science. There are many learning methods, each of which uses a different algorithmic structure to optimize predictions based on the received data. Hence, the first objective of this book will be to shed a light on key principles and methods that are common to both fields.
Machine learning and optimization share three components: representation, evaluation and iterative search. But while the optimization solvers are generally designed to be fast and accurate on implicit models, machine learning methods need to be generic and trained offline on data sets. Machine learning problems present new challenges to the optimization researchers, and machine learning practitioners seek simpler, generic optimization algorithms. This book is thus focused on a common field of research: how to solve new machine learning problems with robust optimization solvers and how to use new optimization methods for existing machine learning problems.
Quite recently, modern approaches to machine learning have also been applied to the design of optimization algorithms themselves, taking advantage of their ability to capture valuable information from complex structures in large spaces. Those capacities appear to be useful, especially for the representation and evaluation components. As large complex structures are ubiquitous in optimization problems and can be used as huge implicit data sets, the use of machine learning allowed improvements in efficiency and genericity of optimization solvers. This book aims at introducing modern advances in algorithm selection, configuration and engineering that rely on machine learning.
Table of Contents
- Introduction
- Optimization Problems
- Machine Learning Problems
- Optimization for Machine Learning
- Machine Learning for Optimization
Guidelines for authors
https://www.openscience.fr/Auteurs
Organization of OpenScience journals
https://www.openscience.fr/Organisation
Address for submission
https://easychair.org/conferences/?conf=oml2018
Evaluation of submissions
Each proposal will be reviewed by at least two double-blind evaluators, who will assess its relevance, scientific validity, originality and clarity of presentation.
Important dates
- Chapter submission: May, 30th, 2018
- Notification to authors: June, 30th, 2018
- Camera-ready papers due: July, 30th, 2018
- Publication: November, 15th, 2018
Publication
Accepted papers will be published in the book of proceedings, PDF format with ISBN & DOI by ISTE & Wiley (index by http://onlinelibrary.wiley.com/), London, UK.