Jacobs School Events
May 14, 2012 12pm - Yahoo! Tech Talk
Location: 4140 EBU3B
Title: Machine Learning in the Bandit Setting: Algorithms, Evaluation, and Case Studies
Much of machine-learning research is about discovering patterns---building intelligent agents that learn to predict future accurately from historical data. While this paradigm has been extremely successful in numerous applications, complex real-world problems such as content recommendation on the Internet often require the agents to learn to act optimally through autonomous interaction with the world they live in, a problem known as reinforcement learning.
Using a news recommendation module on Yahoo!'s front page as a running example, the talk focuses on the special case of contextual bandits that have gained substantial interests recently due to their broad applications. We will highlight a fundamental challenge known as the exploration/exploitation tradeoff, and present a few newly developed algorithms with strong theoretical guarantees. Using a new offline evaluation method, we demonstrate empirical effectiveness of these algorithms for personalizing content recommendation at Yahoo!.
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