Introduction to Online Convex Optimization, second edition

Elad Hazan

ISBN 10: 0262046989 / ISBN 13: 9780262046985

Editore: MIT Press Ltd, US, 2022

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Dati bibliografici

• Titolo: Introduction to Online Convex Optimization, second edition
• Editore: MIT Press Ltd, US
• Data di pubblicazione: 2022
• Lingua: Inglese
• ISBN 10: 0262046989
• ISBN 13: 9780262046985
• Rilegatura: Hardback
• Edizione: seconda edizione
• Condizione: New

Descrizione articolo

Riassunto

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

Informazioni sull?autore

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.