Organisers: Amos Storkey, Jake Abernethy and Mark Reid
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. 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 relationship between scoring rules, market scoring rules, and exponential families 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 crowd-sourcing. 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.
12:00 - 15:00 Lunch.
Additional posters. In addition to posters from the spotlight presentations, we have late breaking and other exciting posters from Ben Moran, Jinli Hu, Chris Berlind, Ruth Urne and Sindhu Kutty.
If you wish to contribute a late breaking poster, please submit it on the NIPSTrans2014 EasyChair site.
We thank Yahoo! Labs and Amazon for sponsoring the workshop.