We use information-theoretic tools to derive a novel analysis of Multi-s...
We revisit binary decision trees from the perspective of partitions of t...
We significantly improve the generalization bounds for VC classes by usi...
Post-hoc feature importance is progressively being employed to explain
d...
We derive a novel information-theoretic analysis of the generalization
p...
Decision trees are popular machine learning models that are simple to bu...
Deep kernel learning provides an elegant and principled framework for
co...
We introduce a new representation learning approach for domain adaptatio...
The Set Covering Machine (SCM) is a greedy learning algorithm that produ...
We propose an extensive analysis of the behavior of majority votes in bi...
We introduce a new representation learning algorithm suited to the conte...
The increased affordability of whole genome sequencing has motivated its...
One of the most tedious tasks in the application of machine learning is ...
We propose a specialized string kernel for small bio-molecules, peptides...
One of the objectives of designing feature selection learning algorithms...