[cs-talks] IVC Seminar on Monday 4/3 - Yiling Chen
Devits, Christopher R
cdevits at bu.edu
Fri Mar 31 13:33:21 EDT 2017
IVC Seminar Announcement
Title: Machine-Learning Aided Incentive Design
Location: Hariri Seminar Room
Date + Time: Monday April 3, 1:00 – 2:00PM
Incentive design is often viewed as an economics problem where the designer would like to “engineer” the rules of interactions so that the behavior of strategic agents leads to some optimal outcome. For example, information elicitation without verification (IEWV) is a classic problem where a principal wants to design reward rules to truthfully elicit high-quality answers of some tasks (e.g. experience of a hotel stay) from strategic agents despite that she cannot evaluate the quality of agents’ contributions. This is also a prevailing problem due to the wide adoption of crowdsourcing. This talk focuses on some of our recent efforts on integrating machine learning techniques into the incentive design for IEWV. In particular, I’ll discuss how we leverage surrogate loss functions to design the reward rules as well as how we develop a bandits framework for aligning long-term incentives.
The talk is based on joint work with Yang Liu.
Bio: Yiling Chen is a Gordon McKay Professor of Computer Science at Harvard University. She received her Ph.D. in Information Sciences and Technology from the Pennsylvania State University in December 2015. Prior to working at Harvard, she spent two years at Yahoo! Research in New York City. Her current research focuses on topics in the intersection of computer science and economics. She was a recipient of NSF Career award and and The Penn State Alumni Association Early Career Award, and was selected by IEEE Intelligent Systems as one of "AI's 10 to Watch" in 2011. She is an associate editor for Journal of Artificial Intelligence Research, ACM Transactions on Economics and Computation and ACM Transactions on Social Computing and has co-chaired the 2013 Conference on Web and Internet Economics (WINE’13) and the 2016 ACM Conference on Economics and Computation (EC’16).
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