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Introduction to Online Convex Optimization, second edition - Couverture rigide

Hazan, Elad

 
9780262046985: Introduction to Online Convex Optimization, second edition
Couverture rigide
ISBN 10 : 0262046989 ISBN 13 : 9780262046985
Editeur : The MIT Press, 2022

Synopsis

New edition of a graduate-level textbook on that focuses on online convex optimization, a machine learning framework that views optimization as a process.

In many practical applications, the environment is so complex that it is not feasible to lay out a comprehensive theoretical model and use classical algorithmic theory and/or mathematical optimization. Introduction to Online Convex Optimization presents a robust machine learning approach that contains elements of mathematical optimization, game theory, and learning theory: an optimization method that learns from experience as more aspects of the problem are observed. This view of optimization as a process has led to some spectacular successes in modeling and systems that have become part of our daily lives.

Based on the “Theoretical Machine Learning” course taught by the author at Princeton University, the second edition of this widely used graduate level text features:

  • Thoroughly updated material throughout
  • New chapters on boosting, adaptive regret, and approachability and expanded exposition on optimization
  • Examples of applications, including prediction from expert advice, portfolio selection, matrix completion and recommendation systems, SVM training, offered throughout
  • Exercises that guide students in completing parts of proofs

    • Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

    À propos de l'auteur

    Elad Hazan is Professor of Computer Science at Princeton University and cofounder and director of Google AI Princeton. An innovator in the design and analysis of algorithms for basic problems in machine learning and optimization, he is coinventor of the AdaGrad optimization algorithm for deep learning, the first adaptive gradient method.

    Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

    Éditeur
    The MIT Press
    Date d'édition
    2022
    Langue
    anglais
    ISBN 10 
    0262046989
    ISBN 13 
    9780262046985
    Reliure
    Relié
    Numéro d'édition
    2
    Nombre de pages
    248
    Coordonnées du fabricant
    non disponible
    Personne responsable
    non disponible