Extreme Classification: A New Paradigm for Ranking & Recommendation

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• Manik Varma (Microsoft Research India)
• Thursday 02 July 2015, 11:00-12:00
• Engineering Department, CBL Room BE-438.

If you have a question about this talk, please contact Dr Jes Frellsen.

Abstract

The objective in extreme multi-label classification is to learn a classifier that can automatically tag a data point with the most relevant subset of labels from a large label set. Extreme multi-label classification is an important research problem since not only does it enable the tackling of applications with many labels but it also allows the reformulation of ranking and recommendation problems with certain advantages over existing formulations.

Our objective, in this talk, is to develop an extreme multi-label classifier that is faster to train and more accurate at prediction than the state-of-the-art Multi-label Random Forest (MLRF) algorithm [Agrawal et al. WWW 13 ] and the Label Partitioning for Sub-linear Ranking (LPSR) algorithm [Weston et al. ICML 13 ]. MLRF and LPSR learn a hierarchy to deal with the large number of labels but optimize task independent measures, such as the Gini index or clustering error, in order to learn the hierarchy. Our proposed FastXML algorithm achieves significantly higher accuracies by directly optimizing an nDCG based ranking loss function. We also develop an alternating minimization algorithm for efficiently optimizing the proposed formulation. Experiments reveal that FastXML can be trained on problems with more than a million labels on a standard desktop in eight hours using a single core and in an hour using multiple cores.

Brief Bio

Manik Varma is a researcher at Microsoft Research India. Manik received a bachelor’s degree in Physics from St. Stephen’s College, University of Delhi in 1997 and another one in Computation from the University of Oxford in 2000 on a Rhodes Scholarship. He then stayed on at Oxford on a University Scholarship and obtained a DPhil in Engineering in 2004. Before joining Microsoft Research, he was a Post-Doctoral Fellow at the Mathematical Sciences Research Institute Berkeley. He has been an Adjunct Professor at the Indian Institute of Technology (IIT) Delhi in the Computer Science and Engineering Department since 2009 and jointly in the School of Information Technology since 2011. His research interests lie in the areas of machine learning, computational advertising and computer vision. He has served as an Area Chair for machine learning and computer vision conferences such as CVPR , ICCV, ICML and NIPS . He has been awarded the Microsoft Gold Star award and has won the PASCAL VOC Object Detection Challenge.

This talk is part of the Machine Learning @ CUED series.

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