Computational Linguistics and Information Processing


The CLIP Colloquium is a weekly speaker series organized and hosted by CLIP Lab. The talks are open to everyone. Most talks are held at 11AM in AV Williams 3258 unless otherwise noted. Typically, external speakers have slots for one-on-one meetings with Maryland researchers before and after the talks; contact the host if you'd like to have a meeting.

If you would like to get on the list or for other questions about the colloquium series, e-mail Jimmy Lin, the current organizer.

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01/30/2013: Human Translation and Machine Translation

Speaker: Philipp Koehn, University of Edinburgh
Time: Wednesday, January 30, 2013, 11:00 AM
Venue: AVW 3258

Despite all the recent successes of machine translation, when it comes to high quality publishable translation, human translators are still unchallenged. Since we can't beat them, can we help them to become more productive? I will talk about some recent work on developing assistance tools for human translators. You can also check out a prototype here and learn about our ongoing European projects CASMACAT and MATECAT.

About the Speaker: Philipp Koehn is Professor of Machine Translation at the School of Informatics at the University of Edinburgh, Scotland. He received his PhD at the University of Southern California and spent a year as postdoctoral researcher at MIT. He is well-known in the field of statistical machine translation for the leading open source toolkit Moses, the organization of the annual Workshop on Statistical Machine Translation and its evaluation campaign as well as the Machine Translation Marathon. He is founding president of the ACL SIG MT and currently serves a vice president-elect of the ACL SIG DAT. He has published over 80 papers and the textbook in the field. He manages a number of EU and DARPA funded research projects aimed at morpho-syntactic models, machine learning methods and computer assisted translation tools.

02/06/2013: Chong Wang: A New Recommender System for Large-scale Document Exploration

How can we help people quickly navigate the vast amount of data and acquire useful knowledge from it? Recommender systems provide a promising solution to this problem. They narrow down the search space by providing a few recommendations that are tailored to users' personal preferences. However, these systems usually work like a black box, limiting further opportunities to provide more exploratory experiences to their users.

In this talk, I will describe how we build a new recommender system for document exploration. Specially, I will talk about two building blocks of the system in detail. The first is about a new probabilistic model for document recommendation that is both predictive and interpretable. It not only gives better predictive performance, but also provides better transparency than traditional approaches. This transparency creates many new opportunities for exploratory analysis---For example, a user can manually adjust her preferences and the system responds to this by changing its recommendations. Second, building a recommender system like this requires learning the probabilistic model from large-scale empirical data. I will describe a scalable approach for learning a wide class of probabilistic models that include our recommendation model as a special case.

Chong is a Project Scientist in Eric Xing's group, Machine Learning Department, Carnegie Mellon University. His PhD advisor was David M. Blei from Princeton University.

02/13/2013: Mona Diab

Speaker: Mona Diab, Columbia University
Time: Wednesday, February 13, 2013, 11:00 AM
Venue: AVW 3258

02/14/2013: Efficient Probabilistic Models for Rankings and Orderings

Speaker: Jon Huang, Stanford University
Time: Thursday, February 14, 2013, 11:00 AM
Venue: ABW 3258

The need to reason probabilistically with rankings and orderings arises in a number of real world problems. Probability distributions over rankings and orderings arise naturally, for example, in preference data, and political election data, as well as a number of less obvious settings such as topic analysis and neurodegenerative disease progression modeling. Representing distributions over the space of all rankings is challenging, however, due to the factorial number of ways to rank a collection of items. The focus of my talk is to discuss methods for combatting this factorial explosion in probabilistic representation and inference.

Ordinarily, a typical machine learning method for dealing with combinatorial complexity might be to exploit conditional independence relations in order to decompose a distribution into compact factors of a graphical model. For ranked data, however, a far more natural and useful probabilistic relation is that of `riffled independence'. I will introduce the concept of riffled independence and discuss how these riffle independent relations can be used to decompose a distribution over rankings into a product of compactly represented factors. These so-called hierarchical riffle-independent distributions are particularly amenable to efficient inference and learning algorithms and in many cases lead to intuitively interpretable probabilistic models. To illustrate the power of exploiting riffled independence, I will discuss a few applications, including Irish political election analysis, visualizing the japanese preferences of sushi types and modeling the progression of Alzheimer's disease, showing results on real datasets in each problem.

This is joint work with Carlos Guestrin (University of Washington), Ashish Kapoor (Microsoft Research) and Daniel Alexander (University College London).

02/27/2013:David Mimno

03/13/2013: Dan Hopkins

03/27/2013: Richard Sproat

04/10/2013: Learning with Marginalized Corrupted Features

Speaker: Kilian Weinberger, Washington University in St. Louis
Time: Wednesday, April 10, 2013, 11:00 AM
Venue: AVW 3258

If infinite amounts of labeled data are provided, many machine learning algorithms become perfect. With finite amounts of data, regularization or priors have to be used to introduce bias into a classifier. We propose a third option: learning with marginalized corrupted features. We corrupt existing data as a means to generate infinitely many additional training samples from a slightly different data distribution -- explicitly in a way that the corruption can be marginalized out in closed form. This leads to machine learning algorithms that are fast, effective and naturally scale to very large data sets. We showcase this technology in two settings: 1. to learn text document representations from unlabeled data and 2. to perform supervised learning with closed form gradient updates for empirical risk minimization.

Text documents (and often images) are traditionally expressed as bag-of-words feature vectors (e.g. as tf-idf). By training linear denoisers that recover unlabeled data from partial corruption, we can learn new data-specific representations. With these, we can match the world-record accuracy on the Amazon transfer learning benchmark with a simple linear classifier. In comparison with the record holder (stacked denoising autoencoders) our approach shrinks the training time from several days to a few minutes.

Finally, we present a variety of loss functions and corrupting distributions, which can be applied out-of-the-box with empirical risk minimization. We show that our formulation leads to significant improvements in document classification tasks over the typically used l_p norm regularization. The new learning framework is extremely versatile, generalizes better, is more stable during test-time (towards distribution drift) and only adds a few lines of code to typical risk minimization.

About the Speaker: Kilian Q. Weinberger is an Assistant Professor in the Department of Computer Science & Engineering at Washington University in St. Louis. He received his Ph.D. from the University of Pennsylvania in Machine Learning under the supervision of Lawrence Saul. Prior to this, he obtained his undergraduate degree in Mathematics and Computer Science at the University of Oxford. During his career he has won several best paper awards at ICML, CVPR and AISTATS. In 2011 he was awarded the AAAI senior program chair award and in 2012 he received the NSF CAREER award. Kilian Weinberger's research is in Machine Learning and its applications. In particular, he focuses on high dimensional data analysis, metric learning, machine learned web-search ranking, transfer- and multi-task learning as well as bio medical applications.

Previous Talks