Pedro M. Q. Aguiar

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We propose a new algorithm for approximate MAP inference on factor graphs, which combines augmented Lagrangian optimization with the dual decomposition method. Each slave subproblem is given a quadratic penalty, which pushes toward faster consensus than in previous subgradient approaches. Our algorithm is provably convergent, parallelizable, and suitable(More)
We present a unified view of two state-of-theart non-projective dependency parsers, both approximate: the loopy belief propagation parser of Smith and Eisner (2008) and the relaxed linear program of Martins et al. (2009). By representing the model assumptions with a factor graph, we shed light on the optimization problems tackled in each method. We also(More)
We propose a distributed algorithm, named Distributed Alternating Direction Method of Multipliers (D-ADMM), for solving separable optimization problems in networks of interconnected nodes or agents. In a separable optimization problem there is a private cost function and a private constraint set at each node. The goal is to minimize the sum of all the cost(More)
Dual decomposition has been recently proposed as a way of combining complementary models, with a boost in predictive power. However, in cases where lightweight decompositions are not readily available (e.g., due to the presence of rich features or logical constraints), the original subgradient algorithm is inefficient. We sidestep that difficulty by(More)
Positive definite kernels on probability measures have been recently applied to classification problems involving text, images, and other types of structured data. Some of these kernels are related to classic information theoretic quantities, such as (Shannon’s) mutual information and the JensenShannon (JS) divergence. Meanwhile, there have been recent(More)
Five hydrogen-oxidizing, thermophilic, strictly chemolithoautotrophic, microaerophilic strains, with similar (99-100%) 16S rRNA gene sequences were isolated from terrestrial hot springs at Furnas, São Miguel Island, Azores, Portugal. The strain, designated Az-Fu1T, was characterized. The motile, 0.9-2.0 microm rods were Gram-negative and non-sporulating.(More)
We propose a distributed algorithm for solving the optimization problem Basis Pursuit (BP). BP finds the least &#x2113;<sub>1</sub>-norm solution of the underdetermined linear system Ax = b and is used, for example, in compressed sensing for reconstruction. Our algorithm solves BP on a distributed platform such as a sensor network, and is designed to(More)
Linear models have enjoyed great success in structured prediction in NLP. While a lot of progress has been made on efficient training with several loss functions, the problem of endowing learners with a mechanism for feature selection is still unsolved. Common approaches employ ad hoc filtering or L1regularization; both ignore the structure of the feature(More)
Training structured predictors often requires a considerable time selecting features or tweaking the kernel. Multiple kernel learning (MKL) sidesteps this issue by embedding the kernel learning into the training procedure. Despite the recent progress towards efficiency of MKL algorithms, the structured output case remains an open research front. We propose(More)
Early approaches to building mosaics by composing photographic images, assume the input images have similar exposures. Since this is unlikely to happen in practice, it became common to compensate for different exposures in the blending step, after the images have been registered, or aligned. However, registration methods usually assume brightness constancy(More)