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NIPS Workshop on Transactional Machine Learning and E-Commerce
David Parkes · David H Wolpert · Jennifer Wortman Vaughan · Jacob D Abernethy · Amos Storkey · Mark Reid · Ping Jin · Nihar Bhadresh Shah · Mehryar Mohri · Luis E Ortiz · Robin Hanson · Aaron Roth · Satyen Kale · Sebastien Lahaie

Fri Dec 12 05:30 AM -- 03:30 PM (PST) @ Level 5; room 510 d
Event URL: http://workshops.inf.ed.ac.uk/ml/nipstransactional/ »

In the context of building a machine learning framework that scales, the current modus operandi is a monolithic, centralised model building approach. These large scale models have different components, which have to be designed and specified in order to fit in with the model as a whole. The result is a machine learning process that needs a grand designer. It is analogous to a planned economy.

There is an alternative. Instead of a centralised planner being in charge of each and every component in the model, we can design incentive mechanisms for independent component designers to build components that contribute to the overall model design. Once those incentive mechanisms are in place, the overall planner need no longer have control over each individual component. This is analogous to a market economy.

The result is a transactional machine learning. The problem is transformed to one of setting up good incentive mechanisms that enable the large scale machine learning models to build themselves. Approaches of this form have been discussed in a number of different areas of research, including machine learning markets, collectives, agent-directed learning, ad-hoc sensor networks, crowdsourcing and distributed machine learning.

It turns out that many of the issues in incentivised transactional machine learning are also common to the issues that turn up in modern e-commerce setting. These issues include issues of mechanism design, encouraging idealised behaviour while modelling for real behaviour, issues surrounding prediction markets, questions of improving market efficiencies, and handling arbitrage, issue on matching both human and machine market interfaces and much more. On the theoretical side, there is a direct relationships between scoring rules, market scoring rules, and exponential family via Bregman Divergences. On the practical side, the issues that turn up in auction design relate to issues regarding efficient probabilistic inference.

The chances for each community to make big strides from understanding the developments in the others is significant. This workshop will bring together those involved in transactional and agent-based methods for machine learning, those involved in the development of methods and theory in e-commerce, those considering practical working algorithms for e-commerce or distributed machine learning and those working on financially incentivised crowdsourcing. The workshop will explore issues around incentivisation, handling combinatorial markets, and developing distributed machine learning. However the primary benefit will be the interaction and informal discussion that will occur throughout the workshop.

This topic is of particular interest because of the increasing importance of machine learning in the e-commerce setting, and the increasing interest in a distributed large scale machine learning. The workshop has some flavour of “multidisciplinary design optimization”: perhaps the optimum of the simultaneous problem of machine learning and e-commerce design is superior to the design found by optimizing each discipline sequentially, since it can exploit the interactions between the disciplines.

The expected outcomes are long lasting interactions between the communities and novel ideas in each individual community gained from learning from the others. The target group of participants are those working in machine learning markets, collectives, agent-directed learning, ad-hoc sensor networks, economic mechanisms in crowdsourcing and distributed machine learning, those working in areas of economics and markets, along with those looking at theory or practice in e-commerce, ad auctions, prediction markets and market design.

Author Information

David Parkes (Harvard University)

David C. Parkes is Gordon McKay Professor of Computer Science in the School of Engineering and Applied Sciences at Harvard University. He was the recipient of the NSF Career Award, the Alfred P. Sloan Fellowship, the Thouron Scholarship and the Harvard University Roslyn Abramson Award for Teaching. Parkes received his Ph.D. degree in Computer and Information Science from the University of Pennsylvania in 2001, and an M.Eng. (First class) in Engineering and Computing Science from Oxford University in 1995. At Harvard, Parkes leads the EconCS group and teaches classes in artificial intelligence, optimization, and topics at the intersection between computer science and economics. Parkes has served as Program Chair of ACM EC’07 and AAMAS’08 and General Chair of ACM EC’10, served on the editorial board of Journal of Artificial Intelligence Research, and currently serves as Editor of Games and Economic Behavior and on the boards of Journal of Autonomous Agents and Multi-agent Systems and INFORMS Journal of Computing. His research interests include computational mechanism design, electronic commerce, stochastic optimization, preference elicitation, market design, bounded rationality, computational social choice, networks and incentives, multi-agent systems, crowd-sourcing and social computing.

David H Wolpert (Santa Fe Institute)
Jennifer Wortman Vaughan (Microsoft Research)
Jennifer Wortman Vaughan

Jenn Wortman Vaughan is a Senior Principal Researcher at Microsoft Research, New York City. Her research background is in machine learning and algorithmic economics. She is especially interested in the interaction between people and AI, and has often studied this interaction in the context of prediction markets and other crowdsourcing systems. In recent years, she has turned her attention to human-centered approaches to transparency, interpretability, and fairness in machine learning as part of MSR's FATE group and co-chair of Microsoft’s Aether Working Group on Transparency. Jenn came to MSR in 2012 from UCLA, where she was an assistant professor in the computer science department. She completed her Ph.D. at the University of Pennsylvania in 2009, and subsequently spent a year as a Computing Innovation Fellow at Harvard. She is the recipient of Penn's 2009 Rubinoff dissertation award for innovative applications of computer technology, a National Science Foundation CAREER award, a Presidential Early Career Award for Scientists and Engineers (PECASE), and a handful of best paper awards. In her "spare" time, Jenn is involved in a variety of efforts to provide support for women in computer science; most notably, she co-founded the Annual Workshop for Women in Machine Learning, which has been held each year since 2006.

Jacob D Abernethy (University of Michigan)
Amos Storkey (University of Edinburgh)
Mark Reid (Apple)
Ping Jin (Microsoft)
Nihar Bhadresh Shah (UC Berkeley)
Mehryar Mohri (Google Research & Courant Institute of Mathematical Sciences)
Luis E Ortiz (University of Michigan - Dearborn)
Robin Hanson (George Mason University)
Aaron Roth (University of Pennsylvania)
Satyen Kale (Google)
Sebastien Lahaie (Google)

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