3 code implementations • 10 Jul 2014 • Aaron J. Defazio, Tibério S. Caetano, Justin Domke
Recent advances in optimization theory have shown that smooth strongly convex finite sums can be minimized faster than by treating them as a black box "batch" problem.
no code implementations • NeurIPS 2012 • Aaron Defazio, Tibério S. Caetano
We consider the case where the structure of the graph to be reconstructed is known to be scale-free.
no code implementations • NeurIPS 2012 • Xianghang Liu, James Petterson, Tibério S. Caetano
Instead of relying on convex losses and regularisers such as in SVMs, logistic regression and boosting, or instead non-convex but continuous formulations such as those encountered in neural networks and deep belief networks, our framework entails a non-convex but \emph{discrete} formulation, where estimation amounts to finding a MAP configuration in a graphical model whose potential functions are low-dimensional discrete surrogates for the misclassification loss.
no code implementations • NeurIPS 2011 • James Petterson, Tibério S. Caetano
The key novelty of our formulation is that we explicitly allow for assortative (submodular) pairwise label interactions, i. e., we can leverage the co-ocurrence of pairs of labels in order to improve the quality of prediction.
no code implementations • NeurIPS 2010 • Novi Quadrianto, James Petterson, Tibério S. Caetano, Alex J. Smola, S. V. N. Vishwanathan
We propose an algorithm to perform multitask learning where each task has potentially distinct label sets and label correspondences are not readily available.
no code implementations • NeurIPS 2010 • James Petterson, Tibério S. Caetano
Multi-label classification is the task of predicting potentially multiple labels for a given instance.
no code implementations • NeurIPS 2010 • James Petterson, Wray Buntine, Shravan M. Narayanamurthy, Tibério S. Caetano, Alex J. Smola
We extend Latent Dirichlet Allocation (LDA) by explicitly allowing for the encoding of side information in the distribution over words.
no code implementations • NeurIPS 2009 • James Petterson, Jin Yu, Julian J. McAuley, Tibério S. Caetano
We present a method for learning max-weight matching predictors in bipartite graphs.
no code implementations • NeurIPS 2009 • Novi Quadrianto, John Lim, Dale Schuurmans, Tibério S. Caetano
The second is a min-min reformulation consisting of fast alternating steps of closed-form updates.
no code implementations • NeurIPS 2008 • Alex J. Smola, Julian J. McAuley, Tibério S. Caetano
Models for near-rigid shape matching are typically based on distance-related features, in order to infer matches that are consistent with the isometric assumption.