Talks & Seminars
Title: Approximating Probabilistic Inference Without Losing Guarantees: Combining Hashing and Feasibility
Mr. Kuldeep Meel, Rice University
Date & Time: August 20, 2014 15:30
Venue: Lecture Hall, B Block, 02nd/03rd Floor, Department of Computer Science and Engineering, Kanwal Rekhi (KReSIT) Building
With the rise of big data and complex modeling, probabilistic inference (i.e. computing probability of an event given some observation) has emerged as a key problem. The current state of art techniques are either exact and face scalability issues or provide very weak approximation guarantees. We introduce a new computational paradigm, which makes only few feasibility queries on models augmented with random constraints, that provides (ϵ-δ) guarantees and scalable practical algorithms. Our new approach builds upon prior work in computational theory and techniques based on universal hashing. I will discuss how we can further use these techniques to generate samples from complex distributions which has been key to constrained-random verification over last two decades.
Speaker Profile:
Kuldeep Meel is a PhD student in Rice working with Prof. Moshe Vardi and Prof. Supratik Chakraborty. 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. He received his M.S. from Rice in 2014 and B.Tech. from IIT Bombay in 2012.
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