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University of Amsterdam, Amsterdam, Netherlands
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High sample efficiency in learning-to-rank can be achieved without the headaches of custom gradients, making RL more accessible for practitioners.
Capturing epistemic uncertainty in click predictions could revolutionize how we model user interactions with rankings, leading to more robust recommendation systems.
