Events

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 cl-colloquium@umiacs.umd.edu list or for other questions about the colloquium series, e-mail Jimmy Lin, the current organizer.

{{#widget:Google Calendar |id=lqah25nfftkqi2msv25trab8pk@group.calendar.google.com |color=B1440E |title=Upcoming Talks |view=AGENDA |height=300 }}

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: A New Recommender System for Large-scale Document Exploration

Speaker: Chong Wang, Carnegie Mellon University
Time: Wednesday, February 6, 2013, 11:00 AM
Venue: AVW 3258

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.

About the Speaker: 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: Computational Modeling of Sociopragmatic Language Use in Arabic and English Social Media

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

Social media language is a treasure trove for mining and understanding human interactions. In discussion fora, people naturally form groups and subgroups aligning along points of consensus and contention. These subgroup formations are quite nuanced as people could agree on some topic such as liking the movie the matrix, but some within that group might disagree on rating the acting skills of Keanu Reeves. Languages manifest these alignments exploiting interesting sociolinguistic devices in different ways. In this talk, I will present our work on subgroup modeling and detection in both Arabic and English social media language. I will share with you our experiences with modeling both explicit and implicit attitude using high and low dimensional feature modeling. This work is the beginning of an interesting exploration into the realm of building computational models of some aspects of the sociopragmatics of human language with the hopes that this research could lead to a better understanding of human interaction.

About the Speaker: Mona Diab is an Associate Professor of Computer Science at the George Washington University. She is also a cofounder of the CADIM (Columbia Arabic Dialect Modeling) group at Columbia University. Mona earned her PhD in Computational Linguistics from University of Maryland College Park with Philip Resnik in 2003 and then did her postdoctoral training with Daniel Jurafsky at Stanford University where she was part of the NLP group. from 2005 till 2012, before joining GWU in Jan of 2013, Mona held the position of Research Scientist/Principle Investigator at Columbia University Center for Computational Learning Systems (CCLS). Mona's research interests span computational lexical semantics, multilingual processing (with a special interest in Arabic and low resource languages), unsupervised learning for NLP, computational sociopragmatic modeling, information extraction and machine translation. Over the past 9 years, Mona has developed significant expertise in modeling low resource languages with a focus on Arabic dialect processing. She is especially interested in ways to leverage existing rich resources to inform algorithms for processing low resource languages. Her research has been published in over 90 papers in various internationally recognized scientific venues. Mona serves as the current elected President of the ACL SIG on Semitic Language Processing, she is also the elected Secretary for the ACL SIG on issues in the Lexicon (SIGLEX). She also serves on the NAACL board as an elected member. Mona recently (2012) co-founded the yearly *SEM conference that attempts to bring together all aspects of semantic processing under the same umbrella venue.

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

Speaker: Jon Huang, Stanford University
Time: Thursday, February 14, 2013, 11:00 AM
Venue: AVW 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: Building Scholarly Methodologies with Large-Scale Topic Analysis

Speaker: David Mimno, Princeton University
Time: Wednesday, February 27, 2013, 9:00 AM
Venue: Hornbake (South Wing) Room 2119

NOTE SPECIAL TIME AND LOCATION!!!

In the last ten years we have seen the creation of massive digital text collections, from Twitter feeds to million-book libraries, all in dozens of languages. At the same time, researchers have developed text mining methods that go beyond simple word frequency analysis to uncover thematic patterns. When we combine big data with powerful algorithms, we enable analysts in many different fields to enhance qualitative perspectives with quantitative measurements. But these methods are only useful if we can apply them at massive scale and distinguish consistent patterns from random variations. In this talk I will describe my work building reliable topic modeling methodologies for humanists, social scientists and science policy officers.

About the Speaker: David Mimno is a postdoctoral researcher in the Computer Science department at Princeton University. He received his PhD from the University of Massachusetts, Amherst. Before graduate school, he served as Head Programmer at the Perseus Project, a digital library for cultural heritage materials, at Tufts University. He is supported by a CRA Computing Innovation fellowship.

03/13/2013: Is Any Politics Local? An Automated Analysis of Mayoral and Gubernatorial Addresses

Speaker: Dan Hopkins, Georgetown University
Time: Wednesday, March 13, 2013, 11:00 AM
Venue: AVW 3258

Dubbed "laboratories of democracy," America's states and its large cities face a wide variety of public policy challenges. But in a period of expanding federal authority and increased long-distance communication, the extent to which U.S. states and large cities pursue varying policy agendas is at once important and unknown. This paper draws on techniques from automated content analysis to measure the major topics in more than 500 "State of the State" and "State of the City" addresses given by American executive officials since 2000. Drawing on the Correlated Topic Model (Blei and Lafferty 2006) and other approaches to topic modeling, it demonstrates that big-city mayors do address a distinctive set of topics from their counterparts in state capitols, but one that is surprisingly consistent across cities. Knowing a mayor's political party provides little leverage on the topics he or she is likely to highlight, while the same is true for objective indicators such as economic conditions or the city's crime rate. At the state level, partisanship proves more predictive of the topics addressed by Governors, but there, too, institutional responsibilities constrain leaders to emphasize a broad and similar set of issues. American political institutions inscribe a substantial role for geographic and institutional differences, but the policy agendas of America's states and largest cities are homogeneous and overlapping.

About the Speaker: Daniel J. Hopkins is an Assistant Professor of Government at Georgetown University whose research focuses on American politics, with a special emphasis on political behavior, urban and local politics, racial and ethnic politics, and statistical methods. Specifically, his research has addressed issues including the role of rhetoric and of local contexts in shaping political behavior. It has also involved the development and application of automated techniques for analyzing political rhetoric. Professor Hopkins' work has appeared in a variety of scholarly and popular outlets, including the American Political Science Review, the American Journal of Political Science, the Journal of Politics, and The Washington Post. Professor Hopkins received his Ph.D. from Harvard University in 2007.

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.

04/24/2013: Matthew Gerber

TBA

Previous Talks

• Fall 2012
• Spring 2012
• Fall 2011
• Spring 2011
• Fall 2010