Title: Machine Learning in the Loop
The traditional supervised machine learning paradigm is inadequate for a wide array of potential machine learning applications where the learning algorithm decides on an action in the real world and gets feedback about that action. This inadequacy results in kludgy systems, such as for ad targeting at internet companies or deep systemic mistrust and skepticism such as for personalized medicine or adaptive clinical trials. I will discuss a new formal basis, algorithms, and practical tricks for doing machine learning in this setting.
John Langford is a computer scientist, working as a senior researcher at Yahoo! Research. He studied Physics and Computer Science at the California Institute of Technology, earning a double bachelor's degree in 1997, and received his Ph.D. from Carnegie Mellon University in 2002. Previously, he was affiliated with the Toyota Technological Institute and IBM's Watson Research Center. He is also the author of the popular Machine Learning weblog, hunch.net and the principle developer of Vowpal Wabbit.