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2014/04/08, MC103, 12:10 - 12:40

The Impossibility of PAC-learning DFAs
Bundit Laekhanukit , PhD Student, McGill SOCS

Abstract:

Probably approximately correct learning (PAC learning) is a learning model proposed by Leslie Valiant that introduces the concepts of computational complexity to machine learning. In particular, given positive and negative samples, the learner is expected to find a polynomial-time computable function that could approximately distinguish between positive and negative samples.

In this work, we study the PAC-learnability of DFAs, i.e., the case where positive and negative samples are drawn from an unknown DFA. We show that, unless NP=RP, DFAs are not PAC-learnable, thus, answering an open question raised 25 years ago by Pitt and Warmuth.

This is a joint work with Parinya Chalermsook and Danupon Nanongkai.