Hi All, We have a tea talk tomorrow by Ian and Yann on their AISTAT submission. Time/Date: 14h30, Thursday Nov. 4th. Location: LISA Lab (AA3256) A Probabilistic Framework for Multi-Instance Learning We present a probabilistic framework for multi-instance learning. Our framework, PMIL, allows a wide variety of supervised learning models to be easily adapted to the multi-instance setting. We demonstrate the effectiveness of our approach on real-world benchmark datasets, achieving state of the art peformance on several TREC tasks. We use controlled experiments with artificially constructed multi-instance problems in order to demonstrate the ability of our approach to learn a good model of individual instances and to empirically analyze its behavior under conditions that violate the assumptions justifying our framework. Cheers, Aaron -- Aaron C. Courville Département d’Informatique et de recherche opérationnelle Université de Montréal email:Aaron.Courville@gmail.com
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Aaron Courville