Abstract:

 We often like to build a model for one scenario based on data from
 similar or nearby cases. For example, consider the problem of building
 a model which predicts a sentiment about books from short reviews,
 using reviews and sentiment of DVDs. Another example is of learning
 movies preference for one viewer from ratings provided by other
 similar users. There is a natural tradeoff between using data from
 more users and using data from only similar users.
 
 In this talk, I will discuss the problem of learning good models using
 data from multiple related or similar sources. I will present a
 theoretical approach which extends the standard probably approximately
 correct (PAC) learning framework, and show how it can be applied in
 order to determine which sources of data should be used and how. The
 bounds explicitly model the inherit tradeoff between building a model
 from many but inaccurate data sources or building it from a few accurate
 data sources. The theory shows that optimal combinations of sources can
 improve performance bounds on some tasks.