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list price: $60.00 USD
edition:Hardcover
category: Computers
published: Dec 2016
ISBN:9780262035644
publisher: The MIT Press

Perturbations, Optimization, and Statistics

contributions by Daniel Tarlow; Andreea Gane; Chansoo Lee; Ian Goodfellow; David Warde-Farley; Stefano Ermon; Ryan Adams; Tamir Hazan; Yury Makarychev; Richard S. Zemel; Percy Liang; Chris J. Maddison; Subhransu Maji; Tommi Jaakkola; Joseph Keshet; Stefan Wager; Jacob Abernethy; George Papandreou; Max Welling; Ambuj Tewari; Alan L. Yuille; William Fithian; Yutian Chen & Konstantin Makarychev

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intelligence (ai) & semantics, computer science
0 of 5
0 ratings
rated!
rated!
list price: $60.00 USD
edition:Hardcover
category: Computers
published: Dec 2016
ISBN:9780262035644
publisher: The MIT Press
Description

A description of perturbation-based methods developed in machine learning to augment novel optimization methods with strong statistical guarantees.

In nearly all machine learning, decisions must be made given current knowledge. Surprisingly, making what is believed to be the best decision is not always the best strategy, even when learning in a supervised learning setting. An emerging body of work on learning under different rules applies perturbations to decision and learning procedures. These methods provide simple and highly efficient learning rules with improved theoretical guarantees. This book describes perturbation-based methods developed in machine learning to augment novel optimization methods with strong statistical guarantees, offering readers a state-of-the-art overview.

Chapters address recent modeling ideas that have arisen within the perturbations framework, including Perturb & MAP, herding, and the use of neural networks to map generic noise to distribution over highly structured data. They describe new learning procedures for perturbation models, including an improved EM algorithm and a learning algorithm that aims to match moments of model samples to moments of data. They discuss understanding the relation of perturbation models to their traditional counterparts, with one chapter showing that the perturbations viewpoint can lead to new algorithms in the traditional setting. And they consider perturbation-based regularization in neural networks, offering a more complete understanding of dropout and studying perturbations in the context of deep neural networks.

About the Authors
Daniel Tarlow is a Researcher at Microsoft Research Cambridge, UK.
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Daniel Tarlow is a Researcher at Microsoft Research Cambridge, UK.
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Daniel Tarlow is a Researcher at Microsoft Research Cambridge, UK.
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Ian Goodfellow is a Research Scientist at Google.
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Ian Goodfellow is a Research Scientist at Google.
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Ian Goodfellow is a Research Scientist at Google.
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Ian Goodfellow is a Research Scientist at Google.
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Tamir Hazan is Assistant Professor at Technion, Israel Institute of Technology.
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Tamir Hazan is Assistant Professor at Technion, Israel Institute of Technology.
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Tamir Hazan is Assistant Professor at Technion, Israel Institute of Technology.
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Tamir Hazan is Assistant Professor at Technion, Israel Institute of Technology.
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Tamir Hazan is Assistant Professor at Technion, Israel Institute of Technology.
Author profile page >

Tamir Hazan is Assistant Professor at Technion, Israel Institute of Technology.
Author profile page >

Tamir Hazan is Assistant Professor at Technion, Israel Institute of Technology.
Author profile page >

Tamir Hazan is Assistant Professor at Technion, Israel Institute of Technology.
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Tamir Hazan is Assistant Professor at Technion, Israel Institute of Technology.
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Tamir Hazan is Assistant Professor at Technion, Israel Institute of Technology.
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George Papandreou is a Research Scientist for Google, Inc.
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George Papandreou is a Research Scientist for Google, Inc.
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George Papandreou is a Research Scientist for Google, Inc.
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Alan Yuille is Professor in the Department of Statistics, University of California, Los Angeles.
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Alan Yuille is Professor in the Department of Statistics, University of California, Los Angeles.
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Alan Yuille is Professor in the Department of Statistics, University of California, Los Angeles.
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Alan Yuille is Professor in the Department of Statistics, University of California, Los Angeles.
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edition:Hardcover
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