{"product_id":"distributionally-robust-learning-9781680837728","title":"Distributionally Robust Learning","description":"\u003cp\u003eMany 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 perturbations\u003c\/p\u003e\u003cp\u003ein the data. \u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eThe 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. \u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cem\u003eDistributionally Robust Learning \u003c\/em\u003eprovides 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.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e \u003ca href=\"https:\/\/sureshotbooks-com.myshopify.com\/search?type=product%2Carticle%2Cpage\u0026amp;q=AUTH-14210677\"\u003eRuidi Chen\u003c\/a\u003e, \u003ca href=\"https:\/\/sureshotbooks-com.myshopify.com\/search?type=product%2Carticle%2Cpage\u0026amp;q=AUTH-14210680\"\u003eIoannis Ch Paschalidis\u003c\/a\u003e\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Now Publishers\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 12\/23\/2020\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 258\u003cbr\u003e\u003cb\u003eBinding Type:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 0.81lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 9.21h x 6.14w x 0.54d\u003cbr\u003e\u003cb\u003eISBN13:\u003c\/b\u003e 9781680837728\u003cbr\u003e\u003cb\u003eISBN10:\u003c\/b\u003e 1680837729\u003cbr\u003e\u003cb\u003eBISAC Categories:\u003c\/b\u003e\u003cbr\u003e- \u003ca href=\"https:\/\/sureshotbooks-com.myshopify.com\/search?type=product%2Carticle%2Cpage\u0026amp;q=CAT-MAT\"\u003eMathematics\u003c\/a\u003e | \u003ca href=\"https:\/\/sureshotbooks-com.myshopify.com\/search?type=product%2Carticle%2Cpage\u0026amp;q=BISAC-MAT042000\"\u003eOptimization\u003c\/a\u003e\u003cbr\u003e- \u003ca href=\"https:\/\/sureshotbooks-com.myshopify.com\/search?type=product%2Carticle%2Cpage\u0026amp;q=CAT-TEC\"\u003eTechnology \u0026amp; Engineering\u003c\/a\u003e | \u003ca href=\"https:\/\/sureshotbooks-com.myshopify.com\/search?type=product%2Carticle%2Cpage\u0026amp;q=BISAC-TEC007000\"\u003eElectrical\u003c\/a\u003e\u003cbr\u003e","brand":"Now Publishers","offers":[{"title":"Default Title","offer_id":44591270199533,"sku":"9781680837728","price":132.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0550\/8097\/6621\/products\/img_6e5df713-9b85-4282-a7da-b8399cab0f87.jpg?v=1702273835","url":"https:\/\/sureshotbooks.com\/products\/distributionally-robust-learning-9781680837728","provider":"SureShot Books Publishing LLC","version":"1.0","type":"link"}