Models of Collective Inference

Thursday, October 22, 2015 - 9:00am - 9:50am
Keller 3-180
In this talk we describe two models of collective inference. The first,
based on joint work with Alexandre Proutière and Mesrob Ohanessian, deals
with categorization of a news item based on the reaction of readers
exposed sequentially. We propose policies for choosing who to expose based
on previous reactions, which achieve a desirable trade-off between
spamming, ie exposure of uninterested readers, and missed opportunity,
ie non-exposure of interested readers.

The second model, based on joint work with Kuang Xu, is motivated by
crowdsourcing. It features experts with distinct abilities and limited
processing power, and inference tasks that consist in labelling an input
with some prescribed confidence level based on noisy expert feedback.
Policies are described which maximize the load of jobs the system can
correctly handle in an asymptotic regime of high confidence level target.