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Events for April 03, 2017
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CS Colloquium: Austin Benson (Stanford) -Tools for higher-order network analysis
Mon, Apr 03, 2017 @ 11:00 AM - 12:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Austin Benson , Stanford University
Talk Title: Tools for higher-order network analysis
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.
Networks are a fundamental model of complex systems in biology, neuroscience, engineering, and social science. Networks are typically described by lower-order connectivity patterns that are captured at the level of individual nodes and edges. However, higher-order connectivity patterns captured by small subgraphs, or network motifs, describe the fundamental structures that control and mediate the behavior of many complex systems. In this talk, I will discuss several higher-order analyses based on higher-order connectivity patterns that I have developed to gain new insights into network data. Specifically, I will introduce a motif-based clustering methodology, a generalization of the classical network clustering coefficient, and a formalism for temporal motifs to study temporal networks. I will also show applications of higher-order analysis in several domains including ecology, biology, transportation, neuroscience, social networks, and human communication.
Biography: Austin Benson is a PhD candidate at Stanford University in the Institute for Computational and Mathematical Engineering where he is advised by Professor Jure Leskovec of the Computer Science Department. His research focuses on developing data-driven methods for understanding complex systems and behavior. Broadly, his research spans the areas of network science, applied machine learning, tensor and matrix computations, and computational social science. Before Stanford, he completed undergraduate degrees in Computer Science and Applied Mathematics at the University of California, Berkeley. Outside of the university, he has spent summers interning at Google (four times), Sandia National Laboratories, and HP Labs.
Host: CS Department
Location: Ronald Tutor Hall of Engineering (RTH) - 217
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor.
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CS Colloquium: Stephan Mandt (Disney Research) - Next generation variational inference: algorithms, models, and applications
Mon, Apr 03, 2017 @ 01:00 PM - 02:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Stephan Mandt, Disney Research
Talk Title: Next generation variational inference: algorithms, models, and applications
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.
Probabilistic modeling is a powerful paradigm in machine learning. In this field, we assume a generative process in order to explain our observations, and then use a Bayesian inference algorithm to reason about its parameters. Probabilistic modeling has become scalable due to stochastic variational inference which reduces Bayesian inference to non-convex stochastic optimization. This talk focuses on two new inference algorithms: variational tempering-an algorithm that operates on several artificial temperatures simultaneously to find better local optima, and constant SGD-a scalable inference algorithm with applications to hyperparameter optimization. I will then present several new models that have become tractable due to modern variational inference with applications in text modeling, recommendations, and computer vision. I will show how a probabilistic view on Google's word2vec algorithm allows for extensions to other types of high dimensional data and show new applications: analyzing supermarket shopping data, movie ratings, and tracking semantic changes of individual words over centuries of digitized books. Finally, I will show how factorized variational autoencoders allow us to analyze audience reactions to movies.
Biography: Stephan Mandt is a research scientist at Disney Research Pittsburgh, where he leads the statistical machine learning group. From 2014 to 2016 he was a postdoctoral researcher with David Blei at Columbia University, and from 2012 to 2014 a PCCM postdoctoral fellow at Princeton University. Stephan did his Ph.D. with Achim Rosch at the Institute for Theoretical Physics at the University of Cologne, supported by a fellowship of the German National Merit Foundation. His research interests include scalable approximate Bayesian inference and machine learning for media analytics.
Host: Fei Sha
Location: Kaprielian Hall (KAP) - 140
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor.
