Stochastic multiplicity automata (SMA) are weighted finite automata that generalize
probabilistic automata. They have been used in the context of probabilistic grammatical …

Probabilistic latent-variable models are a powerful tool for modelling structured data.
However, traditional expectation-maximization methods of learning such models are both …

We show that the general problem of string transduction can be reduced to the problem of
sequence labeling. While character deletions and insertions are allowed in string …

We study the problem of constructing approximations to a weighted automaton. Weighted
finite automata (WFA) are closely related to the theory of rational series. A rational series is a …

We frame max-margin learning of latent variable structured prediction models as a convex
optimization problem, making use of scoring functions computed by input-output observable …

The present paper uses spectral theory of linear operators to construct
approximatelyminimal realizations of weighted languages. Our new contributions are:(i) a …

We derive a spectral method for unsupervised learning ofWeighted Context Free Grammars.
We frame WCFG induction as finding a Hankel matrix that has low rank and is linearly …

This paper studies the following approximate minimization problem: given a minimal
weighted tree automaton A with n states recognizing a weighted tree language f, can we …

We prove the existence of a canonical form for semi-deterministic transducers with sets of
pairwise incomparable output strings. Based on this, we develop an algorithm which learns …

Blackwell's theory of approachability provides fundamental results for repeated games with
vector-valued payoffs, which have been usefully applied in the theory of learning in games …