International Conference on Machine Learning (ICML), 21-26 June 2014, Beijing, China
Xavier Carreras, presenting: "Spectral Regularization for Max-Margin Sequence Tagging", co-authorized with Ariadna Quattoni, Borja Balle and Amir Globerson.
Abstract: 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 operator models. This learning problem can be expressed as an optimization involving a low-rank Hankel matrix that represents the input-output operator model. The direct outcome of our work is a new spectral regularization method for max-margin structured prediction. Our experiments confirm that our proposed regularization framework leads to an effective way of controlling the capacity of structured prediction models.