4 papers
Improving Generalization Bounds for VC Classes Using the Hypergeometric Tail Inversion
Jean-Samuel Leboeuf, Frédéric LeBlanc, Mario Marchand
We significantly improve the generalization bounds for VC classes by using two main ideas. First, we consider the hypergeometric tail inversion to obtain a very tight non-uniform d…
Decision trees as partitioning machines to characterize their generalization properties
Jean-Samuel Leboeuf, Frédéric LeBlanc, Mario Marchand
Decision trees are popular machine learning models that are simple to build and easy to interpret. Even though algorithms to learn decision trees date back to almost 50 years, key…
Adaptive Deep Kernel Learning
Prudencio Tossou, Basile Dura, Francois Laviolette +2
Deep kernel learning provides an elegant and principled framework for combining the structural properties of deep learning algorithms with the flexibility of kernel methods. By mea…
Greedy Biomarker Discovery in the Genome with Applications to Antimicrobial Resistance
Alexandre Drouin, Sébastien Giguère, Maxime Déraspe +3
The Set Covering Machine (SCM) is a greedy learning algorithm that produces sparse classifiers. We extend the SCM for datasets that contain a huge number of features. The whole gen…