Stern, David and Herbrich, Ralf and Graepel, Thore and Samulowitz, Horst and Pulina, Luca and Tacchella, Armando (2010) Collaborative expert portfolio management. In: AAAI 2010: 24th AAAI Conference on Artificial Intelligence, 11-15 July 2010, Atlanta (GA), USA., AAAI Press. p. 179-184. ISBN 978-1-57735-463-5. Conference or Workshop Item.
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We consider the task of assigning experts from a portfolio of specialists in order to solve a set of tasks. We apply a Bayesian model which combines collaborative filtering with a feature-based description of tasks and experts to yield a general framework for managing a portfolio of experts. The model learns an embedding of tasks and problems into a latent space in which affinity is measured by the inner product. The model can be trained incrementally and can track non-stationary data, tracking potentially changing expert and task characteristics. The approach allows us to use a principled decision theoretic framework for expert selection, allowing the user to choose a utility function that best suits their objectives. The model component for taking into account the performance feedback data is pluggable, allowing flexibility. We apply the model to manage a portfolio of algorithms to solve hard combinatorial problems. This is a well studied area and we demonstrate a large improvement on the state of the art in one domain (constraint solving) and in a second domain (combinatorial auctions) created a portfolio that performed significantly better than any single algorithm.
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