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Talks & Seminars
Sampling from Combinatorial Spaces: Achieving the Fine Balancing Act Between Independence and Scalability
Mr. Kuldeep Singh Meel, Rice University
Date & Time: May 20, 2015 15:00
Venue: Lecture Hall, B Block, 02nd/03rd Floors, Department of Computer Science and Engineering, Kanwal Rekhi (KReSIT) building
Abstract:
Sampling from combinatorial spaces, typically represented by a set of constraints, is one of the fundamental problems in computer science with numerous applications, including probabilistic reasoning, machine learning, planning, statistical physics, inexact computing, and constrained-random verification. The current state of the art techniques either fail to scale to large spaces or provide no guarantees of quality of sampling. We introduce a new computational paradigm based on universal hashing, where we carefully trade independence to achieve scalability without losing theoretical guarantees. I will discuss how we can further use this new paradigm to generate sample from complex distributions which has been key to constrained-random verification over last two decades. This talk discusses further advances beyond what we discussed in an earlier talk presented last year.
Speaker Profile:
Kuldeep is a PhD student in Rice working with Prof. Moshe Vardi and Supratik Chakraborty. He obtained B. Tech. from IIT Bombay and M. S. from Rice in 2012 and 2014 respectively. His research broadly falls into the intersection of probabilistic reasoning, computer-aided verification and formal methods. He is the recipient of 2013-14 Andrew Ladd fellowship. His masters thesis won 2014 Vienna Center of Logic and Algorithms Outstanding Masters thesis award.
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