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Introduction to Online Convex Optimization, second edition by Elad Hazan at Abbey's Bookshop,
Introduction to Online Convex Optimization, second edition
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Elad Hazan

Introduction to Online Convex Optimization, second edition

Elad Hazan

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English
MIT Press
01 November 2022
Optimisation
Series: Adaptive Computation and Machine Learning series
"New edition of a graduate-level textbook on that focuses on online convex optimization, a machine learning framework that views optimization as a process.

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"
By:  
Imprint:   MIT Press
Country of Publication:   United States
Dimensions:   Height: 229mm,  Width: 152mm, 
Weight:   567g
ISBN:   9780262046985
ISBN 10:   0262046989
Series:   Adaptive Computation and Machine Learning series
Pages:   256
Publication Date:  
Audience:   General/trade ,  ELT Advanced
Format:   Hardback
Publisher's Status:   Active
Preface xi Acknowledgments xv List of Figures xvii List of Symbols xix 1 Introduction 1 2 Basic Concepts in Convex Optimization 15 3 First-Order Algorithms for Online Convex Optimization 37 4 Second-Order Methods 49 5 Regularization 63 6 Bandit Convex Optimization 89 7 Projection-Free Algorithms 107 8 Games, Duality and Regret 123 9 Learning Theory, Generalization, and Online Convex Optimization 133 10 Learning in Changing Environments 147 11 Boosting and Regret 163 12 Online Boosting 171 13 Blackwell Approachability and Online Convex Optimization 181 Notes 191 References 193 Index 207

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.

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