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Machine Learning in Automated Mechanism Design for Pricing and Auctions
Nina Balcan · Tuomas Sandholm · Ellen Vitercik

Tue Jul 10 12:15 AM -- 02:30 AM (PDT) @ A9

Mechanism design is a field of game theory with tremendous real-world impact, encompassing areas such as pricing and auction design. A powerful approach in this field is automated mechanism design, which uses machine learning and optimization to design mechanisms based on data. This automated approach helps overcome challenges faced by traditional, manual approaches to mechanism design, which have been stuck for decades due to inherent computational complexity challenges: the revenue-maximizing mechanism is not known even for just two items for sale! In this tutorial, we cover the rapidly growing area of automated mechanism design for revenue maximization. This encompasses both the foundations of batch and online learning (including statistical guarantees and optimization procedures), as well as real-world success stories.

URL: https://sites.google.com/view/amdtutorial

Author Information

Nina Balcan (Carnegie Mellon University)
Nina Balcan

Maria-Florina Balcan is an Associate Professor in the School of Computer Science at Carnegie Mellon University. Her main research interests are machine learning and theoretical computer science. Her honors include the CMU SCS Distinguished Dissertation Award, an NSF CAREER Award, a Microsoft Faculty Research Fellowship, a Sloan Research Fellowship, and several paper awards. She has served as a Program Committee Co-chair for COLT 2014, a Program Committee Co-chair for ICML 2016, and a board member of the International Machine Learning Society.

Tuomas Sandholm (Carnegie Mellon University)

Tuomas Sandholm is Angel Jordan Professor of Computer Science at Carnegie Mellon University. He is Founder and Director of the Electronic Marketplaces Laboratory. He has published over 450 papers. With his student Vince Conitzer, he initiated the study of automated mechanism design in 2001. In parallel with his academic career, he was Founder, Chairman, and CTO/Chief Scientist of CombineNet, Inc. from 1997 until its acquisition in 2010. During this period the company commercialized over 800 of the world's largest-scale generalized combinatorial multi-attribute auctions, with over $60 billion in total spend and over $6 billion in generated savings. He is Founder and CEO of Optimized Markets, Strategic Machine, and Strategy Robot. Also, his algorithms run the UNOS kidney exchange, which includes 69% of the transplant centers in the US. He has developed the leading algorithms for several general classes of game. The team that he leads is the two-time world champion in computer Heads-Up No-Limit Texas Hold’em poker, and Libratus became the first and only AI to beat top humans at that game. Among his many honors are the NSF Career Award, inaugural ACM Autonomous Agents Research Award, Sloan Fellowship, Carnegie Science Center Award for Excellence, Edelman Laureateship, Newell Award for Research Excellence, and Computers and Thought Award. He is Fellow of the ACM, AAAI, and INFORMS. He holds an honorary doctorate from the University of Zurich.

Ellen Vitercik (Carnegie Mellon University)

Ellen Vitercik is a PhD student in computer science at Carnegie Mellon University. Her primary research interests are artificial intelligence, machine learning, theoretical computer science, and computational economics. Her honors include a National Science Foundation Graduate Research Fellowship and a Microsoft Research Women's Fellowship.

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