Amaury Yang's Books

Metric Learning

By: Aurélien Muise,Amaury Yang

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Similarity between objects plays an important role in both human cognitive processes and artificial systems for recognition and categorization. How to appropriately measure such similarities for a given task is crucial to the performance of many machine learning, pattern recognition and data mining methods. This book is devoted to metric learning, a set of techniques to automatically learn similarity and distance functions from data that has attracted a lot of interest in machine learning and related fields in the past ten years. In this book, we provide a thorough review of the metric learning literature that covers algorithms, theory and applications for both numerical and structured data. We first introduce relevant definitions and classic metric functions, as well as examples of their use in machine learning and data mining. We then review a wide range of metric learning algorithms, starting with the simple setting of linear distance and similarity learning. We show how one may scale-up these methods to very large amounts of training data. To go beyond the linear case, we discuss methods that learn nonlinear metrics or multiple linear metrics throughout the feature space, and review methods for more complex settings such as multi-task and semi-supervised learning. Although most of the existing work has focused on numerical data, we cover the literature on metric learning for structured data like strings, trees, graphs and time series. In the more technical part of the book, we present some recent statistical frameworks for analyzing the generalization performance in metric learning and derive results for some of the algorithms presented earlier. Finally, we illustrate the relevance of metric learning in real-world problems through a series of successful applications to computer vision, bioinformatics and information retrieval. Table of Contents: Introduction / Metrics / Properties of Metric Learning Algorithms / Linear Metric Learning / Nonlinear and Local Metric Learning / Metric Learning for Special Settings / Metric Learning for Structured Data / Generalization Guarantees for Metric Learning / Applications / Conclusion / Bibliography / Authors' Biographies

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Page Count

139

Publisher

Springer Nature

Published Date

ISBN 10

303101572X

ISBN 13

9783031015724

Anchored in Resilience

Overcoming Adversity through Mental Health Awareness

By: Amaury Ponciano

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Anchored in Resilience

Overcoming Adversity through Mental Health Awareness

By: Amaury Ponciano

The journey of a man, father, and sailor, Anchored in Resilience: Overcoming Adversity through Mental Health Awareness is the true story of adventures, lessons, and growth as seen through the eyes of boy looking for love, a young man looking for answers, and an adult yearning to inspire. Author and Navy veteran Amaury Ponciano shares vulnerable truths, revelations, and experiences from over twenty years of Naval service. Anchored in Resilience is a memoir of healing—because healing starts with understanding our wounds.

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149

Publisher

Gatekeeper Press

Published Date

ISBN 10

1662947224

ISBN 13

9781662947223

Advances in Domain Adaptation Theory

By: Ievgen Redko,Emilie Morvant,Amaury Habrard,Marc Sebban,Younès Bennani

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Advances in Domain Adaptation Theory

By: Ievgen Redko,Emilie Morvant,Amaury Habrard,Marc Sebban,Younès Bennani

Advances in Domain Adaptation Theory gives current, state-of-the-art results on transfer learning, with a particular focus placed on domain adaptation from a theoretical point-of-view. The book begins with a brief overview of the most popular concepts used to provide generalization guarantees, including sections on Vapnik-Chervonenkis (VC), Rademacher, PAC-Bayesian, Robustness and Stability based bounds. In addition, the book explains domain adaptation problem and describes the four major families of theoretical results that exist in the literature, including the Divergence based bounds. Next, PAC-Bayesian bounds are discussed, including the original PAC-Bayesian bounds for domain adaptation and their updated version. Additional sections present generalization guarantees based on the robustness and stability properties of the learning algorithm.

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Page Count

208

Publisher

ISTE Press - Elsevier

Published Date

ISBN 10

178548236X

ISBN 13

9781785482366

Managing Country Risk in an Age of Globalization

A Practical Guide to Overcoming Challenges in a Complex World

By: Michel Henry Bouchet,Charles A. Fishkin,Amaury Goguel

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Managing Country Risk in an Age of Globalization

A Practical Guide to Overcoming Challenges in a Complex World

By: Michel Henry Bouchet,Charles A. Fishkin,Amaury Goguel

This book provides an up-to-date guide to managing Country Risk. It tackles its various and interlinked dimensions including sovereign risk, socio-political risk, and macroeconomic risk for foreign investors, creditors, and domestic residents. It shows how they are accentuated in the global economy together with new risks such as terrorism, systemic risk, environmental risk, and the rising trend of global volatility and contagion. The book also assesses the limited usefulness of traditional yardsticks of Country Risk, such as ratings and rankings, which at best reflect the market consensus without predictive value and at worst amplify risk aversion and generate crisis contamination. This book goes further than comparing a wide range of risk management methods in that it provides operational and forward-looking warning signs of Country Risk. The combination of the authors’ academic and market-based backgrounds makes the book a useful tool for scholars, analysts, and practitioners.

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Page Count

542

Publisher

Springer

Published Date

ISBN 10

3319897527

ISBN 13

9783319897523

Book's by Amaury Yang

Metric Learning

Metric Learning

Anchored in Resilience

Anchored in Resilience

Advances in Domain Adaptation Theory

Advances in Domain Adaptation Theory

Managing Country Risk in an Age of Globalization

Managing Country Risk in an Age of Globalization

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