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On KL divergence and beyond

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If you have a question about this talk, please contact Edoardo Maria Ponti .

Abstract: Many machine learning tasks require fitting a model to observed data, which is mostly done via divergence minimisation. In this talk I will start from the basics and discuss the celebrated Kullback-Leibler (KL) divergence and its applications in machine learning. Then I will discuss potential issues of KL divergence and motivates other divergence measures. I will show how f-divergence, a rich family of divergences that includes KL, is applied to machine learning tasks, in particular for approximate inference. If time permits, I will briefly touch on divergencies/discrepancies that are not density-ratio based, and discuss relavent applications.

This talk is part of the Language Technology Lab Seminars series.

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Buckingham House Lecture Theatre, New Hall, University of Cambridge, Huntingdon Road, Cambridge Gordon Lab Seminar Room, Austin Building, Dept. of Materials Science & Metallurgy University Centre, Granta Place, Mill Lane, Cambridge CB2 1RU

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