no code implementations • NeurIPS 2012 • Amr Ahmed, Sujith Ravi, Alex J. Smola, Shravan M. Narayanamurthy
Clustering is a key component in data analysis toolbox.
no code implementations • NeurIPS 2012 • Nan Du, Le Song, Ming Yuan, Alex J. Smola
However, the underlying transmission networks are often hidden and incomplete, and we observe only the time stamps when cascades of events happen.
no code implementations • NeurIPS 2010 • Gilbert Leung, Novi Quadrianto, Kostas Tsioutsiouliklis, Alex J. Smola
We present a fast online solver for large scale maximum-flow problems as they occur in portfolio optimization, inventory management, computer vision, and logistics.
no code implementations • NeurIPS 2010 • Martin Zinkevich, Markus Weimer, Lihong Li, Alex J. Smola
With the increase in available data parallel machine learning has become an increasingly pressing problem.
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, 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 • Martin Zinkevich, John Langford, Alex J. Smola
Online learning algorithms have impressive convergence properties when it comes to risk minimization and convex games on very large problems.
no code implementations • NeurIPS 2009 • Novi Quadrianto, James Petterson, Alex J. Smola
Many transductive inference algorithms assume that distributions over training and test estimates should be related, e. g. by providing a large margin of separation on both sets.
no code implementations • NeurIPS 2008 • Novi Quadrianto, Le Song, Alex J. Smola
Object matching is a fundamental operation in data analysis.
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.
no code implementations • NeurIPS 2008 • Xinhua Zhang, Le Song, Arthur Gretton, Alex J. Smola
Many machine learning algorithms can be formulated in the framework of statistical independence such as the Hilbert Schmidt Independence Criterion.
no code implementations • NeurIPS 2008 • Olivier Chapelle, Chuong B. Do, Choon H. Teo, Quoc V. Le, Alex J. Smola
Large-margin structured estimation methods work by minimizing a convex upper bound of loss functions.