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list price: $60.00 USD
edition:Hardcover
also available: Paperback
category: Computers
published: Sep 2011
ISBN:9780262016469
publisher: The MIT Press

Optimization for Machine Learning

contributions by Julien Mairal; Guillaume Obozinski; Ryota Tomioka; Dimitri Bertsekas; Masashi Sugiyama; Shie Mannor; Amir Globerson; Vijay Krishnamurthy; Yoshua Bengio; Jean-Yves Audibert; Francis Bach; Arkadi Nemirovski; Sören Sonnenburg; Léon Bottou; Suvrit Sra; Sebastian Nowozin; Elad Hazan; Rémi Munos; Joachim Dahl; Alexandre d'Aspremont; Dongmin Kim; Olivier Bousquet; David Sontag; Shiqian Ma; Nicolas Le Roux; Andrew Fitzgibbon; Constantine Caramanis; Sébsatien Bubeck; Taiji Suzuki; Tommi Jaakkola; Lieven Vandenberghe; Stephen J. Wright; Anatoli Juditsky; Katya Scheinberg; Martin Andersen; Selin Damla Ahipasaoglu; Mark Schmidt; Jacek Gondzio; Rodolphe Jenatton; Thomá Werner; Huan Xu; Vojtech Franc & Zhang Liu

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intelligence (ai) & semantics, robotics
0 of 5
0 ratings
rated!
rated!
list price: $60.00 USD
edition:Hardcover
also available: Paperback
category: Computers
published: Sep 2011
ISBN:9780262016469
publisher: The MIT Press
Description

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.

About the Authors

Julien Mairal

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Guillaume Obozinski

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Ryota Tomioka

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Dimitri Bertsekas

Dimitri P. Bertsekas is Professor of Electrical Engineering and Computer Science at MIT.
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Masashi Sugiyama is Associate Professor in the Department of Computer Science at Tokyo Institute of Technology.
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Masashi Sugiyama is Associate Professor in the Department of Computer Science at Tokyo Institute of Technology.
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Masashi Sugiyama is Associate Professor in the Department of Computer Science at Tokyo Institute of Technology.
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Masashi Sugiyama is Associate Professor in the Department of Computer Science at Tokyo Institute of Technology.
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Yoshua Bengio is Professor of Computer Science at the Université de Montréal.
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Yoshua Bengio is Professor of Computer Science at the Université de Montréal.
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Yoshua Bengio is Professor of Computer Science at the Université de Montréal.
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Yoshua Bengio is Professor of Computer Science at the Université de Montréal.
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Yoshua Bengio is Professor of Computer Science at the Université de Montréal.
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Léon Bottou is a Research Scientist at NEC Labs America.
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Suvrit Sra is a Research Scientist at the Max Planck Institute for Biological Cybernetics, Tübingen, Germany.
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Sebastian Nowozin is a Researcher in the Machine Learning and Perception group (MLP) at Microsoft Research, Cambridge, England.
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Sebastian Nowozin is a Researcher in the Machine Learning and Perception group (MLP) at Microsoft Research, Cambridge, England.
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Sebastian Nowozin is a Researcher in the Machine Learning and Perception group (MLP) at Microsoft Research, Cambridge, England.
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Sebastian Nowozin is a Researcher in the Machine Learning and Perception group (MLP) at Microsoft Research, Cambridge, England.
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Sebastian Nowozin is a Researcher in the Machine Learning and Perception group (MLP) at Microsoft Research, Cambridge, England.
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Sebastian Nowozin is a Researcher in the Machine Learning and Perception group (MLP) at Microsoft Research, Cambridge, England.
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Sebastian Nowozin is a Researcher in the Machine Learning and Perception group (MLP) at Microsoft Research, Cambridge, England.
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Sebastian Nowozin is a Researcher in the Machine Learning and Perception group (MLP) at Microsoft Research, Cambridge, England.
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Sebastian Nowozin is a Researcher in the Machine Learning and Perception group (MLP) at Microsoft Research, Cambridge, England.
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Sebastian Nowozin is a Researcher in the Machine Learning and Perception group (MLP) at Microsoft Research, Cambridge, England.
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Sebastian Nowozin is a Researcher in the Machine Learning and Perception group (MLP) at Microsoft Research, Cambridge, England.
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Sebastian Nowozin is a Researcher in the Machine Learning and Perception group (MLP) at Microsoft Research, Cambridge, England.
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Sebastian Nowozin is a Researcher in the Machine Learning and Perception group (MLP) at Microsoft Research, Cambridge, England.
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Sebastian Nowozin is a Researcher in the Machine Learning and Perception group (MLP) at Microsoft Research, Cambridge, England.
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Sebastian Nowozin is a Researcher in the Machine Learning and Perception group (MLP) at Microsoft Research, Cambridge, England.
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Sebastian Nowozin is a Researcher in the Machine Learning and Perception group (MLP) at Microsoft Research, Cambridge, England.
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Stephen J. Wright is Professor of Computer Science at the University of Wisconsin–Madison.
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Stephen J. Wright is Professor of Computer Science at the University of Wisconsin–Madison.
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Stephen J. Wright is Professor of Computer Science at the University of Wisconsin–Madison.
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Stephen J. Wright is Professor of Computer Science at the University of Wisconsin–Madison.
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Stephen J. Wright is Professor of Computer Science at the University of Wisconsin–Madison.
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Stephen J. Wright is Professor of Computer Science at the University of Wisconsin–Madison.
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Stephen J. Wright is Professor of Computer Science at the University of Wisconsin–Madison.
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Stephen J. Wright is Professor of Computer Science at the University of Wisconsin–Madison.
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Stephen J. Wright is Professor of Computer Science at the University of Wisconsin–Madison.
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Stephen J. Wright is Professor of Computer Science at the University of Wisconsin–Madison.
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Stephen J. Wright is Professor of Computer Science at the University of Wisconsin–Madison.
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Stephen J. Wright is Professor of Computer Science at the University of Wisconsin–Madison.
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Recommended Age, Grade, and Reading Levels
Age:
18 to 100
Grade:
13 to 17

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