Elad Hazan

Elad Hazan is Professor of Computer Science at Princeton University and cofounder and director of Google AI Princeton.

  • Introduction to Online Convex Optimization, Second Edition

    Introduction to Online Convex Optimization, Second Edition

    Elad Hazan

    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; expanded exposition on optimization; examples of applications, including prediction from expert advice, portfolio selection, matrix completion and recommendation systems, and SVM training, offered throughout; and exercises that guide students in completing parts of proofs.

    • Hardcover $60.00


  • Optimization for Machine Learning

    Optimization for Machine Learning

    Suvrit Sra, Sebastian Nowozin, and Stephen J. Wright

    An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities.

    The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields.Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity, size, and variety of today's machine learning models call for the reassessment of existing assumptions. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods. It also devotes attention to newer themes such as regularized optimization, robust optimization, gradient and subgradient methods, splitting techniques, and second-order methods. Many of these techniques draw inspiration from other fields, including operations research, theoretical computer science, and subfields of optimization. The book will enrich the ongoing cross-fertilization between the machine learning community and these other fields, and within the broader optimization community.

    • Hardcover $60.00
    • Paperback $50.00