Suvrit Sra

Sebastian Nowozin

Stephen J. Wright

Francis Bach

Rodolphe Jenatton

Julien Mairal

Guillaume Obozinski

Martin Andersen

Joachim Dahl

Zhang Liu

Lieven Vandenberghe

Dimitri Bertsekas

Anatoli Juditsky

Arkadi Nemirovski

Vojtech Franc

Soeren Sonnenburg

Thomáš Werner

David Sontag

Amir Globerson

Tommi Jaakkola

Ryota Tomioka

Taiji Suzuki

Masashi Sugiyama

Elad Hazan

Mark Schmidt

Dongmin Kim

Jacek Gondzio

Léon Bottou

Olivier Bousquet

Constantine Caramanis

Shie Mannor

Huan Xu

Nicolas Le Roux

Yoshua Bengio

Andrew Fitzgibbon

Jean-Yves Audibert

Sébsatien Bubeck

Rémi Munos

Katya Scheinberg

Shiqian Ma

Vijay Krishnamurthy

Selin Damla Ahipasaoglu

Alexandre d'Aspremont

Michael I. Jordan

Thomas G. Dietterich PhD

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.

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Optimization for Machine Learning (Neural Information Processing series)

著者：Thomas G. Dietterich PhDの検索結果

Optimization for Machine Learning (Neural Information Processing series)