Max-Planck-Institut für Informatik
max planck institut
mpii logo Minerva of the Max Planck Society

MPI-INF or MPI-SWS or Local Campus Event Calendar

<< Previous Entry Next Entry >> New Event Entry Edit this Entry Login to DB (to update, delete)
What and Who
Title:From Proteins to Robots: Learning to Optimize with Confidence
Speaker:Andreas Krause
coming from:ETH Zürich
Speakers Bio: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.
Event Type:INF Distinguished Lecture Series
Visibility:D1, D2, D3, D4, D5, RG1, SWS, MMCI
We use this to send out email in the morning.
Level:Public Audience
Date, Time and Location
Date:Thursday, 21 April 2016
Duration:60 Minutes
Building:E1 4
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.
Name(s):Connie Balzert
Phone:0681 9325-2000
Video Broadcast
Video Broadcast:NoTo Location:
Tags, Category, Keywords and additional notes
Attachments, File(s):
Created by:Connie Balzert/MPI-INF, 04/14/2016 11:34 AMLast modified by:Uwe Brahm/MPII/DE, 11/24/2016 04:13 PM
  • Connie Balzert, 04/14/2016 11:34 AM -- Created document.