{"product_id":"learning-and-decision-making-from-rank-data-paperback","title":"Learning and Decision-Making from Rank Data - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eLirong Xia\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThe ubiquitous challenge of learning and decision-making from rank data arises in situations where intelligent systems collect preference and behavior data from humans, learn from the data, and then use the data to help humans make efficient, effective, and timely decisions. Often, such data are represented by \u003ci\u003erankings\u003c\/i\u003e.\u003c\/p\u003e \u003cp\u003eThis book surveys some recent progress toward addressing the challenge from the considerations of statistics, computation, and socio-economics. We will cover classical statistical models for rank data, including random utility models, distance-based models, and mixture models. We will discuss and compare classical and state-of-the-art algorithms, such as algorithms based on Minorize-Majorization (MM), Expectation-Maximization (EM), Generalized Method-of-Moments (GMM), rank breaking, and tensor decomposition. We will also introduce principled Bayesian preference elicitation frameworks for collecting rank data. Finally, we will examine socio-economic aspects of statistically desirable decision-making mechanisms, such as Bayesian estimators.\u003c\/p\u003e \u003cp\u003eThis book can be useful in three ways: (1) for theoreticians in statistics and machine learning to better understand the considerations and caveats of learning from rank data, compared to learning from other types of data, especially cardinal data; (2) for practitioners to apply algorithms covered by the book for sampling, learning, and aggregation; and (3) as a textbook for graduate students or advanced undergraduate students to learn about the field.\u003c\/p\u003e \u003cp\u003eThis book requires that the reader has basic knowledge in probability, statistics, and algorithms. Knowledge in social choice would also help but is not required.\u003c\/p\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003eDr. Xia is an associate professor in the Department of Computer Science at Rensselaer Polytechnic Institute (RPI). Prior to joining RPI in 2013, he was a CRCS fellow and NSF CI Fellow at the Center for Research on Computation and Society at Harvard University. He received his Ph.D. in ComputerScience and M.A. in Economics from Duke University, and his B.E. in Computer Science and Technology from Tsinghua University. His research focuses on the intersection of com-puter science and microeconomics. Dr. Xia is the recipient of an NSF CAREER award, a Simons-Berkeley Research Fellowship, the 2018 Rensselaer James M. Tien â (TM)66 Early Career Award, and was named as one of \"\"AIâ (TM)s 10 to watch\"\" by IEEE Intelligent Systems in 2015.\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 143\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.34 x 9.25 x 7.5 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e February 06, 2019\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":47212674842873,"sku":"9783031004544","price":97.18,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0789\/2782\/3097\/files\/lQj0pIky-q9783031004544.webp?v=1768094976","url":"https:\/\/bookscloud.io\/products\/learning-and-decision-making-from-rank-data-paperback","provider":"BooksCloud Book Dropshipping","version":"1.0","type":"link"}