|Speaker :||Nidhi Hegde|
|Nokia Bell Labs|
|Time:||2:00 pm - 3:00 pm|
|Location:||LINCS Seminars room|
We study adaptive matching in expert systems, as an instance of adaptive sequential hypothesis testing. Examples of such systems include Q&A platforms, crowdsourcing, image classification. Consider a system that receives tasks or jobs to be classified into one of a set of given types. The system has access to a set of workers, or experts, and the expertise of a worker is defined by the jobs he is able to classify and the error in his response. This active sequential hypothesis testing problem was first addressed by Chernoff in 1959, whereby experts to be queried are selected according to how much information they provide. In this talk we will begin with an overview of past work on this topic, then consider our model where we assume access to less fine-grained information about the expertise of workers. We propose a gradient-based algorithm, show its optimality and through numerical results show that it outperforms the Chernoff-like algorithms.