We propose a formulation of the Decision Tree learning algorithm in the Compression settings and derive tight generalization error bounds. In particular, we propose Sample Compres...
We propose a general framework for support vector machines (SVM) based on the principle of multi-objective optimization. The learning of SVMs is formulated as a multiobjective pro...
In this work, a novel mobile browser for geo-referenced pictures is introduced and described. We use the term browser to denote a system aimed at browsing pictures selected from a...
We present an algorithm for active learning (adaptive selection of training data) within the context of semi-supervised multi-task classifier design. The semi-supervised multi-ta...
We describe a scalable and general-purpose framework for auto-tuning compiler-generated code. We combine Active Harmony’s parallel search backend with the CHiLL compiler transfo...
Ananta Tiwari, Chun Chen, Jacqueline Chame, Mary W...