## Rawski on Learning with Partially Ordered Sets

Jon Rawski is presenting his work on learning with partially ordered representation in the IACS seminar room this Wednesday, November 14, 1-2pm. Check it out (for the undecided: there’ll be free lunch)!

**Title:**
Learning with Partially Ordered Representations

**Abstract:**
When generalizing, and making compact hypotheses from sparse and
impoverished data, learning algorithms often rely on domain-specific
representations or features. Often these features pose a learning problem,
by exponentially increasing the number of hypotheses which correctly
describe data. However, features also give the learner an advantage, by
structuring the space of hypotheses in a particular way. This structure
gives rise to certain entailments between grammars, which I show using
several linguistic examples - text recognition, speech analysis, and
syntactic adjunction. I discuss how learners can exploit this structure to
make inferences, and introduce a non-statistical learning method that
provably identifies the responsible constraints. Integration and comparison
of these insights into statistical learning is ongoing research.