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GRASP Special Seminar: Brian Kulis, Ohio State University, “Small-Variance Asymptotics for Large-Scale Learning”

December 2, 2014 @ 2:00 pm - 3:00 pm

Abstract: It is widely known that the Gaussian mixture model is related to k-means by
“small-variance asymptotics”: as the covariances of the clusters shrink, the
EM algorithm approaches the k-means algorithm and the negative
log-likelihood approaches the k-means objective. Similar asymptotic
connections exist for other machine learning models, including
dimensionality reduction (probabilistic PCA becomes PCA), multiview learning
(probabilistic CCA becomes CCA), and classification (a restricted Bayes
optimal classifier becomes the SVM). The asymptotic non-probabilistic
counterparts to the probabilistic models are almost always more scalable,
and are typically easier to analyze, making them useful alternatives to the
probabilistic models in many situations. I will explore how we can extend
such asymptotics to a richer class of probabilistic models, with a focus on
large-scale graphical models, Bayesian nonparametric models, and time-series
data. I will develop the necessary mathematical tools needed for these
extensions and will describe a framework for designing scalable optimization
problems derived from the rich probabilistic models. Applications are
diverse, and include topic modeling, network evolution, and deep feature


- Learn More

Brian Kulis is an assistant professor of computer science at Ohio State
University. His research focuses on machine learning, statistics, computer
vision, data mining, and large-scale optimization. Previously, he was a
postdoctoral fellow at UC Berkeley EECS and was also affiliated with the
International Computer Science Institute. He obtained his PhD in computer
science from the University of Texas in 2008, and his BA degree from Cornell
University in computer science and mathematics in 2003. For his research,
he has won three best student paper awards at top-tier conferences—two at
the International Conference on Machine Learning (in 2005 and 2007) and one
at the IEEE Conference on Computer Vision and Pattern Recognition (in 2008).
He was also the recipient of an MCD graduate fellowship from the University
of Texas (2003-2007) and an Award of Excellence from the College of Natural
Sciences at the University of Texas.


December 2, 2014
2:00 pm - 3:00 pm
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