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Yingzhen Li, Microsoft Research Cambridge 
Thursday 01 November 2018, 11:00-12:00
On KL divergence and beyond
- đ¤ Speaker: Yingzhen Li, Microsoft Research Cambridge đ Website
- đ Date & Time: Thursday 01 November 2018, 11:00 - 12:00
- đ Venue: Boardroom, Faculty of English, West Road
Abstract
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.
Series This talk is part of the Language Technology Lab Seminars series.
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