Popularity Prediction for Social Media over Arbitrary Time Horizons
Daniel Haimovich, Dima Karamshuk, Thomas Leeper, Evgeniy Riabenko, Milan Vojnovic
Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising
Journal of Machine Learning Research (JMLR)
Abstract
This work shows how to leverage causal inference to understand the behavior of complex learning systems interacting with their environment and predict the consequences of changes to the system. Such predictions allow both humans and algorithms to select the changes that would have improved the system performance. This work is illustrated by experiments on the ad placement system associated with the Bing search engine.
Latest Publications
VLDB - 2022
Sustainable AI: Environmental Implications, Challenges and Opportunities
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