Graduate Seminar with Yu Zhang: Learning Graphical Model from Distributed Streams

Date/Time
Date(s) - 28 Aug 2019
1:10 PM - 2:00 PM

Location
3043 ECpE Building Addition

Speaker: Yu Zhang, ECpE Graduate Student

Adviser: Srikanta Tirthapura

Title: Learning Graphical Model from Distributed Streams

Abstract: A current challenge for data management systems is to support the construction and maintenance of machine learning models over data that is large, multi-dimensional, and evolving. While systems that could support these tasks are emerging, the need to scale to distributed, streaming data requires new models and algorithms. In this setting, as well as computational scalability and model accuracy, we also need to minimize the amount of communication between distributed processors, which is the chief component of latency. We study Bayesian Networks, the workhorse of graphical models, and present a communication-efficient method for continuously learning and maintaining a Bayesian network model over data that is arriving as a distributed stream partitioned across multiple processors. We show a strategy for maintaining model parameters that leads to an exponential reduction in communication when compared with baseline approaches to maintain the exact MLE (maximum likelihood estimation). Meanwhile, our strategy provides similar prediction errors for the target distribution and for classification tasks.

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Interested in giving a seminar? Contact Professor Namrata Vaswani.

Announcements and publicity by Kristin Clague, and scheduling by the department secretary.