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Condición: New.

Hardcover. Condición: new. Hardcover. New edition of a graduate-level textbook on that focuses on online convex optimization, a machine learning framework that views optimization as a process.New edition of a graduate-level textbook on that focuses on online convex optimization, a machine learning framework that views optimization as a process.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 throughoutNew chapters on boosting, adaptive regret, and approachability and expanded exposition on optimizationExamples of applications, including prediction from expert advice, portfolio selection, matrix completion and recommendation systems, SVM training, offered throughoutExercises that guide students in completing parts of proofs "This book describes a machine learning framework that contains elements of mathematical optimization, game theory, and computational learning theory"-- Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condición: As New. Unread book in perfect condition.

Condición: New.

Condición: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.

HRD. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Condición: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Condición: Very Good. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Hardback. Condición: New.

Hardcover. Condición: New. Brand new book, sourced directly from publisher. Dispatch time is 24-48 hours from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Condición: As New. Unread book in perfect condition.

hardcover. Condición: New. Brand new book, sourced directly from publisher. Dispatch time is 24-48 hours from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Condición: New.

Condición: New.

Hardback. Condición: New. This tutorial guide introduces online nonstochastic control, an emerging paradigm in control of dynamical systems and differentiable reinforcement learning that applies techniques from online convex optimization and convex relaxations to obtain new methods with provable guarantees for classical settings in optimal and robust control. In optimal control, robust control, and other control methodologies that assume stochastic noise, the goal is to perform comparably to an offline optimal strategy. In online control, both cost functions and perturbations from the assumed dynamical model are chosen by an adversary. Thus, the optimal policy is not defined a priori and the goal is to attain low regret against the best policy in hindsight from a benchmark class of policies. The resulting methods are based on iterative mathematical optimization algorithms and are accompanied by finite-time regret and computational complexity guarantees. This book is ideal for graduate students and researchers interested in bridging classical control theory and modern machine learning.…

Condición: New.

Hardback. Condición: New. This tutorial guide introduces online nonstochastic control, an emerging paradigm in control of dynamical systems and differentiable reinforcement learning that applies techniques from online convex optimization and convex relaxations to obtain new methods with provable guarantees for classical settings in optimal and robust control. In optimal control, robust control, and other control methodologies that assume stochastic noise, the goal is to perform comparably to an offline optimal strategy. In online control, both cost functions and perturbations from the assumed dynamical model are chosen by an adversary. Thus, the optimal policy is not defined a priori and the goal is to attain low regret against the best policy in hindsight from a benchmark class of policies. The resulting methods are based on iterative mathematical optimization algorithms and are accompanied by finite-time regret and computational complexity guarantees. This book is ideal for graduate students and researchers interested in bridging classical control theory and modern machine learning.…

Condición: New.

Condición: New.

Condición: New.

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Hardcover. Condición: New. ISBN:9780262046985.

Condición: New. 2022. Hardcover. . . . . .

Hardcover. Condición: Brand New. 174 pages. 6.00x0.44x9.00 inches. In Stock.

Condición: New. In English.

Hardcover. Condición: Brand New. 2nd edition. 256 pages. 9.00x6.00x0.63 inches. In Stock.

Condición: As New. Unread book in perfect condition.

Hardback. Condición: New. New copy - Usually dispatched within 3 working days.