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).