NIPS Workshop on Transactional Machine Learning and E-Commerce

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.