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What and Who
Title:Constrained Counting and Sampling: Bridging the Gap between Theory and Practice
Speaker:Kuldeep Meel
coming from:Rice University
Speakers Bio:Kuldeep Meel is a final year PhD candidate in Rice University working with Prof. Moshe Vardi and Prof. Supratik Chakraborty. His research
broadly lies at the intersection of artificial intelligence and formal methods. He is the recipient of a 2016-17 IBM PhD Fellowship, the 2016-17
Lodieska Stockbridge Vaughn Fellowship and the 2013-14 Andrew Ladd Fellowship. His research won the best student paper award at the
International Conference on Constraint Programming 2015. He obtained a B.Tech. from IIT Bombay and an M.S. from Rice in 2012 and 2014
respectively. He co-won the 2014 Vienna Center of Logic and Algorithms International Outstanding Masters thesis award.
Event Type:SWS Colloquium
Visibility:SWS, RG1, MMCI
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Level:AG Audience
Language:English
Date, Time and Location
Date:Tuesday, 7 March 2017
Time:10:00
Duration:90 Minutes
Location:Saarbr├╝cken
Building:E1 5
Room:029
Abstract
Constrained counting and sampling are two fundamental problems in Computer Science with numerous applications, including network reliability, privacy, probabilistic reasoning, and constrained-random verification. In constrained counting, the task is to compute the total weight, subject to a given weighting function, of the set of solutions of the given constraints. In constrained sampling, the task is to sample randomly, subject to a given weighting function, from the set of solutions to a set of given constraints.

In this talk, I will introduce a novel algorithmic framework for constrained sampling and counting that combines the classical algorithmic technique of universal hashing with the dramatic progress made in Boolean reasoning over the past two decades. This has allowed us to obtain breakthrough results in constrained sampling and counting, providing a new algorithmic toolbox in machine learning, probabilistic reasoning, privacy, and design verification. I will demonstrate the utility of the above techniques on various real applications including probabilistic inference, design verification and our ongoing collaboration in estimating the reliability of critical infrastructure networks during natural disasters.

Contact
Name(s):Annika Meiser
Phone:9303 9105
EMail:--email address not disclosed on the web
Video Broadcast
Video Broadcast:YesTo Location:Kaiserslautern
To Building:G26To Room:111
Meeting ID:
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Created:
Annika Meiser/MPI-SWS, 02/24/2017 11:58 AM
Last modified:
Uwe Brahm/MPII/DE, 03/07/2017 07:01 AM
  • Annika Meiser, 02/24/2017 12:15 PM -- Created document.