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Generalization bounds for learning weighted automata

Research output: Contribution to journalJournal articlepeer-review

<mark>Journal publication date</mark>15/03/2018
<mark>Journal</mark>Theoretical Computer Science
Number of pages18
Pages (from-to)89-106
Publication StatusPublished
Early online date1/12/17
<mark>Original language</mark>English


This paper studies the problem of learning weighted automata from a finite sample of strings with real-valued labels. We consider several hypothesis classes of weighted automata defined in terms of three different measures: the norm of an automaton's weights, the norm of the function computed by an automaton, and the norm of the corresponding Hankel matrix. We present new data-dependent generalization guarantees for learning weighted automata expressed in terms of the Rademacher complexity of these classes. We further present upper bounds on these Rademacher complexities, which reveal key new data-dependent terms related to the complexity of learning weighted automata.