Bundle methods for machine learning (2009)
We present a globally convergent method for regularized risk minimization problems. Our method applies to Support Vector estimation, regression, Gaussian Processes, and any other regularized risk...
Learning Graph Matching (2008)
Tibério S. Caetano, Li Cheng, Quoc V. Le, Alex J. Smola
As a fundamental problem in pattern recognition, graph matching has found a variety of applications in the field of computer vision. In graph matching, patterns are modeled as graphs and pattern...
1 A Short Tour of Kernel Methods for Graphs (2008)
Thomas Gärtner, Fraunhofer Ais. Kd, Schloß Birlinghoven, Sankt Augustin, Quoc V. Le, Alex J Smola
Machine learning research has – apart from some exceptions – originally concentrated on learning from data that can naturally be represented in a single table without links between the instances....
Learning Graph Matching (2008)
Caetano, Tiberio S., McAuley, Julian J., Cheng, Li, Le, Quoc V., Smola, Alex J.
As a fundamental problem in pattern recognition, graph matching has applications in a variety of fields, from computer vision to computational biology. In graph matching, patterns are modeled as...
Estimating Labels from Label Proportions (2008)
Quadrianto, Novi, Smola, Alex J., Caetano, Tiberio S., Le, Quoc V.
Consider the following problem: given sets of unlabeled observations, each set with known label proportions, predict the labels of another set of observations, also with known label proportions. This...
Learning Graph Matching (2008)
Tibério S. Caetano, Li Cheng, Quoc V. Le, Alex J. Smola
As a fundamental problem in pattern recognition, graph matching has found a variety of applications in the field of computer vision. In graph matching, patterns are modeled as graphs and pattern...
Nonparametric quantile estimation (2006)
Ichiro Takeuchi, Quoc V. Le, Timothy D. Sears, Alexander J. Smola, Chris Williams
In regression, the desired estimate of y|x is not always given by a conditional mean, although this is most common. Sometimes one wants to obtain a good estimate that satisfies the property that a...
Simpler knowledge-based support vector machines (2006)
If appropriately used, prior knowledge can significantly improve the predictive accuracy of learning algorithms or reduce the amount of training data needed. In this paper we introduce a simple...
Heteroscedastic gaussian process regression (2005)
This paper presents an algorithm to estimate simultaneously both mean and variance of a non parametric regression problem. The key point is that we are able to estimate variance locally unlike...
Statistical Machine Learning Program, Canberra (2005)
Quoc V. Le, Tim Sears, Alexander J. Smola
In regression, the desired estimate of y|x is not always given by a conditional mean, although this is most common. Sometimes one wants to obtain a good estimate that satisfies the property that a...
Statistical Machine Learning Program, Canberra (2005)
Quoc V. Le, Tim Sears, Alexander J. Smola
In regression, the desired estimate of y|x is not always given by a conditional mean, although this is most common. Sometimes one wants to obtain a good estimate that satisfies the property that a...