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Events for April 02, 2015
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CS Colloquium: Guy van den Broeck (KU Leuven) - Scalable Inference and Learning for High-Level Probabilistic Models
Thu, Apr 02, 2015 @ 09:45 AM - 10:50 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Guy van den Broeck, KU Leuven
Talk Title: Scalable Inference and Learning for High-Level Probabilistic Models
Series: CS Colloquium
Abstract: Probabilistic graphical models are pervasive in AI and machine learning. A recent push, however, is towards more high-level representations of uncertainty, such as probabilistic programs, probabilistic databases, and statistical relational models. This move is akin to going from hardware circuits to a full-fledged programming language, and poses key challenges for inference and learning. For instance, we encounter a fundamental limitation of classical learning algorithms: they make strong independence assumptions about the entities in the data (e.g., images, web pages, patients, etc.). These assumptions fail to hold in a global view of the data, where all entities are related. We also encounter a limitation of existing reasoning algorithms, which fail to scale to large, densely connected graphical models, consisting of millions of interrelated entities.
In this talk, I present my research on efficient algorithms for high-level probabilistic models, called lifted inference and learning algorithms. I begin by introducing the key principles behind exact lifted inference, namely to exploit symmetry and exchangeability in the model. Next, I discuss the strengths and limitations of lifting. Building on results from database theory and counting complexity, I identify classes of tractable models, and classes where high-level reasoning is fundamentally hard. I conclude by showing the practical embodiment of these ideas, in the form of approximate inference and learning algorithms that scale up to big data and big models.
The lecture will be available to stream HERE
Biography: Guy Van den Broeck graduated summa cum laude with a Ph.D. in Computer Science from KU Leuven, Belgium, in 2013. He was a postdoctoral researcher at UCLA and KU Leuven. His research interests are broadly in machine learning, artificial intelligence, knowledge representation and reasoning, and statistical relational learning. His work was awarded the ECCAI AI Dissertation Award 2014, Scientific Prize IBM Belgium for Informatics 2014, and Alcatel-Lucent Innovation Award 2009. He is the recipient of the best student paper award at ILP 2011 and a best paper honorable mention at AAAI 2014. For more information, see http://guyvandenbroeck.com
Host: Computer Science Department
Webcast: https://bluejeans.com/442226528Location: Olin Hall of Engineering (OHE) - 132
WebCast Link: https://bluejeans.com/442226528
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Big Data and Data Science: Some Hype but Real Opportunities
Thu, Apr 02, 2015 @ 05:00 PM - 06:00 PM
Thomas Lord Department of Computer Science
University Calendar
Big Data and Data Science: Some Hype but Real Opportunities
IMSC Seminar – Host: Cyrus Shahabi
April 2 - 5:00-6:00pm
SAL-101
Speaker: Michael Franklin, UC Berkeley Computer Science
Abstract
Data is all the rage across industry and across campuses. While it may be temping to dismiss the buzz as just another spin of the hype cycle, there are substantial shifts and realignments underway that are fundamentally changing how Computer Science, Statistics and virtually all subject areas will be taught, researched, and perceived as disciplines. In this talk I will give my personal perspectives on this new landscape based on experiences organizing a large, industry-engaged academic Computer Science research project (the AMPLab), in helping to establish a campus-wide Data Science research initiative (the Berkeley Institute for Data Science), and my participation on a campus task force charged with mapping out Data Science Education for all undergraduates at Berkeley. I will make the case that there are real opportunities across campus in both education and research, and that Data Science should be viewed as an emerging discipline in its own right.
Bio
Michael Franklin is the Thomas M. Siebel Professor of Computer Science and Chair of the Computer Science Division at the University of California, Berkeley. Prof. Franklin is also the Director of the Algorithms, Machines, and People Laboratory (AMPLab) at UC Berkeley. The AMPLab currently works with 27 industrial sponsors including founding sponsors Amazon Web Services, Google, and SAP. AMPLab is well-known for creating a number of popular systems in the Open Source Big Data ecosystem including Spark, Mesos, GraphX and MLlib, all parts of the Berkeley Data Analytics Stack (BDAS). Prof. Franklin is a co-PI and Executive Committee member for the Berkeley Institute for Data Science, part of a multi-campus initiative to advance Data Science Environments. He is an ACM Fellow, a two-time winner of the ACM SIGMOD "Test of Time" award, has several "Best Paper" awards and two CACM Research Highlights selections, and is recipient of the outstanding Advisor Award from the Computer Science Graduate Student Association at Berkeley.
Location: Henry Salvatori Computer Science Center (SAL) - 101
WebCast Link: https://bluejeans.com/952662854
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
