Ariadna Quattoni

ICML Conference 2014 Conference Paper

Spectral Regularization for Max-Margin Sequence Tagging

• Ariadna Quattoni
• Borja Balle
• Xavier Carreras
• Amir Globerson

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.

NeurIPS Conference 2013 Conference Paper

Unsupervised Spectral Learning of Finite State Transducers

• Raphael Bailly
• Xavier Carreras
• Ariadna Quattoni

Finite-State Transducers (FST) are a standard tool for modeling paired input-output sequences and are used in numerous applications, ranging from computational biology to natural language processing. Recently Balle et al. presented a spectral algorithm for learning FST from samples of aligned input-output sequences. In this paper we address the more realistic, yet challenging setting where the alignments are unknown to the learning algorithm. We frame FST learning as finding a low rank Hankel matrix satisfying constraints derived from observable statistics. Under this formulation, we provide identifiability results for FST distributions. Then, following previous work on rank minimization, we propose a regularized convex relaxation of this objective which is based on minimizing a nuclear norm penalty subject to linear constraints and can be solved efficiently.

ICML Conference 2012 Conference Paper

Local Loss Optimization in Operator Models: A New Insight into Spectral Learning

• Borja Balle
• Ariadna Quattoni
• Xavier Carreras

ICML Conference 2009 Conference Paper

An efficient projection for l 1, infinity regularization

• Ariadna Quattoni
• Xavier Carreras
• Michael Collins 0001
• Trevor Darrell

NeurIPS Conference 2004 Conference Paper

Conditional Random Fields for Object Recognition

• Ariadna Quattoni
• Michael Collins
• Trevor Darrell

We present a discriminative part-based approach for the recognition of object classes from unsegmented cluttered scenes. Objects are modeled as flexible constellations of parts conditioned on local observations found by an interest operator. For each object class the probability of a given assignment of parts to local features is modeled by a Conditional Ran- dom Field (CRF). We propose an extension of the CRF framework that incorporates hidden variables and combines class conditional CRFs into a unified framework for part-based object recognition. The parameters of the CRF are estimated in a maximum likelihood framework and recogni- tion proceeds by finding the most likely class under our model. The main advantage of the proposed CRF framework is that it allows us to relax the assumption of conditional independence of the observed data (i. e. local features) often used in generative approaches, an assumption that might be too restrictive for a considerable number of object classes.