Speaker Leon Bottou
Host Ofer Dekel
Affiliation MS Ad Center Science Team
Date recorded 26 October 2012
This work shows how to leverage causal inference to understand the behavior of com- plex learning systems interacting with their environment and predict the consequences of changes to the system. Such predictions allow both humans and algorithms to select changes that improve both the short-term and long-term performance of such systems. This work is illustrated by experiments carried out on the ad placement system associated with the Bing search engine.
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