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What and Who

From Proteins to Robots: Learning to Optimize with Confidence

Andreas Krause
ETH Zürich
INF Distinguished Lecture Series

Andreas Krause is an Associate Professor of Computer Science at ETH Zurich, where he leads the Learning & Adaptive Systems Group. Before that he was an Assistant Professor of Computer Science at Caltech. He received his Ph.D. and M.Sc. 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 a Kavli Frontiers Fellow of the US National Academy of Sciences. He received an ERC Starting Investigator grant, the Deutscher Mustererkennungspreis, an NSF CAREER award, the Okawa Foundation Research Grant recognizing top young researchers in telecommunications as well as the ETH Golden Owl teaching award. His research in learning and adaptive systems that actively acquire information, reason and make decisions in large, distributed and uncertain domains, such as sensor networks and the Web received awards at several premier conferences and journals.
AG 1, AG 2, AG 3, AG 4, AG 5, RG1, SWS, MMCI  
Public Audience
English

Date, Time and Location

Thursday, 21 April 2016
14:30
60 Minutes
E1 4
024
Saarbrücken

Abstract

With the success of machine learning, we increasingly see learning algorithms make decisions in the real world. Often, however, this is in stark contrast to the classical train-test paradigm, since the learning algorithm affects the very data it must operate on.  I will explain how predictive confidence bounds can guide data acquisition in a principled way to make effective decisions in a variety of complex settings.  I will present algorithms with performance guarantees relying on the notion of submodularity, a natural notion of diminishing returns.  I will also discuss several applications, ranging from autonomously guiding wetlab experiments in protein structure optimization, to safe automatic parameter tuning on a robotic platform.

Contact

Connie Balzert
0681 9325-2000
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Connie Balzert, 04/14/2016 11:34 -- Created document.