Learning and Decision-Making from Rank Data

Lirong Xia

ISBN 10: 303100454X / ISBN 13: 9783031004544

Published by Springer, Springer Feb 2019, 2019

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -The 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 rankings.This 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.This 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.This book requires that the reader has basic knowledge in probability, statistics, and algorithms. Knowledge in social choice would also help but is not required.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 160 pp. Englisch.

Seller Inventory # 9783031004544

Bibliographic Details

• Title: Learning and Decision-Making from Rank Data
• Publisher: Springer, Springer Feb 2019
• Publication Date: 2019
• Language: English
• ISBN 10: 303100454X
• ISBN 13: 9783031004544
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 191H x 9D millimeters 235 millimeters width by 191 millimeters height by 9 millimeters depth
• Weight: 312 grams

About this title

Synopsis

The 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 rankings.

This 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.

This 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.

This book requires that the reader has basic knowledge in probability, statistics, and algorithms. Knowledge in social choice would also help but is not required.

About the Author

Dr. 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 ’66 Early Career Award, and was named as one of ""AI’s 10 to watch"" by IEEE Intelligent Systems in 2015.