Distributionally Robust Learning by Ruidi Chen (English) Paperback Book

Description: Distributionally Robust Learning by Ruidi Chen, Ioannis Ch. Paschalidis Presents a comprehensive statistical learning framework that uses Distributionally Robust Optimization (DRO) under the Wasserstein metric to ensure robustness to perturbationsin the data. The authors introduce the reader to the fundamental properties of the Wasserstein metric and the DRO formulation, before explaining the theory in detail. FORMAT Paperback LANGUAGE English CONDITION Brand New Publisher Description Many of the modern techniques to solve supervised learning problems suffer from a lack of interpretability and analyzability that do not give rise to rigorous mathematical results. This monograph develops a comprehensive statistical learning framework that uses Distributionally Robust Optimization (DRO) under the Wasserstein metric to ensure robustness to perturbationsin the data. The authors introduce the reader to the fundamental properties of the Wasserstein metric and the DRO formulation, before explaining the theory in detail and its application. They cover a series of learning problems, including (i) distributionally robust linear regression; (ii) distributionally robust regression with group structure in the predictors; (iii) distributionally robust multi-output regression and multiclass classification; (iv) optimal decision making that combines distributionally robust regression with nearest-neighbor estimation; (v) distributionally robust semi-supervised learning; (vi) distributionally robust reinforcement learning. Throughout the monograph, the authors use applications in medicine and health care to illustrate the theoretical ideas in practice. They include numerical experiments and case studies using synthetic and real data. Distributionally Robust Learning provides a detailed insight into a technique that has gained a lot of recent interest in developing robust supervised learning solutions that are founded in sound mathematical principles. It will be enlightening for researchers, practitioners and students working on the optimization of machine learning systems. Table of Contents 1. Introduction2. The Wasserstein Metric3. Solving the Wasserstein DRO Problem4. Distributionally Robust Linear Regression5. Distributionally Robust Grouped Variable Selection6. Distributionally Robust Multi-Output Learning7. Optimal Decision Making via Regression Informed K-NN8. Advanced Topics in Distributionally Robust Learning9. Discussion and ConclusionsAcknowledgmentsReferences Details ISBN1680837729 Author Ioannis Ch. Paschalidis Language English Year 2020 ISBN-10 1680837729 ISBN-13 9781680837728 Format Paperback Publication Date 2020-12-23 DOI 10.1561/2400000026 Pages 256 UK Release Date 2020-12-23 Imprint now publishers Inc Place of Publication Hanover Country of Publication United States AU Release Date 2020-12-23 NZ Release Date 2020-12-23 US Release Date 2020-12-23 Publisher now publishers Inc Series Foundations and Trends® in Optimization Alternative 9781680837735 Audience Professional & Vocational We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:138196574;

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