Interactive Machine Learning via Adaptive Submodularity

Andreas Krause (ETH Zurich, Switzerland)

Abstract:

How can people and machines cooperate to gain insight and discover useful information from complex data sets? A central challenge lies in optimizing the interaction, which leads to difficult sequential decision problems under uncertainty. In this talk, I will introduce the new concept of adaptive submodularity, generalizing the classical notion of submodular set functions to adaptive policies. We prove that if a problem satisfies this property, a simple adaptive greedy algorithm is guaranteed to be competitive with the optimal policy. The concept allows to recover, generalize and extend existing results in diverse domains including active learning and resource allocation. I will show results on several real-world applications, ranging from interactive content search over image categorization in citizen science to biodiversity monitoring via conservation drones.

Bio:

Andreas Krause is an Assistant Professor of Computer Science at ETH Zurich, where he leads the Learning & Adaptive Systems Group (since 2011). Before that he was Assistant Professor of Computer Science at Caltech (2009-2012). He received his Ph.D. in Computer Science from Carnegie Mellon University (2008) and his Diplom in Computer Science and Mathematics from the Technical University of Munich, Germany (2004). He is a Microsoft Research Faculty Fellow, and received an ERC Starting Investigator grant, the Deutscher Mustererkennungspreis, an NSF CAREER award as well as the ETH Golden Owl teaching award. His research in learning and adaptive systems received awards at several premier conferences (AAAI, KDD, IPSN, ICML, UAI) and journals (JAIR, JWRPM).