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Learning and Decision-Making from Rank Data

Lirong Xia (author) Ronald Brachman (Series edited by)
Francesca Rossi (Series edited by)
Peter Stone (Series edited by)

ISBN: 9781681734422

Publication Date: Feb 2019

Format: Hardback

Also available as: Paperback  

Surveys some recent progress toward addressing the challenge from the considerations of statistics, computation, and socio-economics. The book covers classical statistical models for rank data, including random utility models, distance-based models, and mixture models, and discusses classical and state of-the-art algorithms.
£79.50

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  • Full Description
  • More Information
  • Table of Contents
  • Author Biography
  • Customer Reviews
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.
Pages 159
Dimensions 235 x 190
Date Published 28 Feb 2019
Publisher Morgan & Claypool Publishers
Series Synthesis Lectures on Artificial Intelligence and Machine Learning
Subject/s Information Science & Technology   Computer science   Computing: general  
  • Preface
  • Acknowledgments
  • Introduction
  • Statistical Models for Rank Data
  • Parameter Estimation Algorithms
  • The Rank-Breaking Framework
  • Mixture Models for Rank Data
  • Bayesian Preference Elicitation
  • Socially Desirable Group Decision-Making from Rank Data
  • Future Directions
  • Bibliography
  • Author's Biography
Dr Lirong 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 Computer Science 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 computer 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.

Ronald Brachman, Jacobs Technion-Cornell Institute at Cornell Tech.

Francesca Rossi AI Ethics Global Leader, IBM Research AI.

Peter Stone University of Texas at Austin.

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