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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.
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 unless otherwise noted. Typically, external speakers have slots for one-on-one meetings with Maryland researchers.
If you would like to get on the
cl- firstname.lastname@example.org list or for other questions about the colloquium series, e-mail [mailto: email@example.com Jimmy Lin], the current organizer.
If you would like to get on the -@umiacs.umd.edu list or for other questions about the colloquium series, e-mail [mailto:@umd.edu ], the current organizer.
|title= Upcoming Talks
== 01/30/2013: Human Translation and Machine Translation ==
about on the [http://www...edu/about-/]
* on [://./=]
'''Speaker:''' [http://homepages.inf.ed.ac.uk/pkoehn/ Philipp Koehn], University of Edinburgh<br/>
'''Time:''' Wednesday, January 30, 2013, 11:00 AM<br/>
'''Venue:''' AVW 3258<br/>
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 [http://www.caitra.org/ here]
and learn about our ongoing European projects [http://www.casmacat.eu/ CASMACAT]
and [http://www.matecat.com/ 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:''' [http://www. cs. cmu.edu/ ~chongw/ Chong Wang], Carnegie Mellon University<br/>
'''Time:''' Wednesday, February 6, 2013, 11:00 AM<br/>
'''Venue:''' AVW 3258<br/>
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:''' [http://www1.ccls.columbia.edu/~mdiab/ Mona Diab] , Columbia University<br/>
'''Time:''' Wednesday, February 13, 2013, 11:00 AM<br/>
'''Venue:''' AVW 3258<br/>
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:''' [http://stanford.edu/~jhuang11/ Jon Huang], Stanford University<br/>
'''Time:''' Thursday, February 14, 2013, 11:00 AM<br/>
'''Venue:''' AVW 3258<br/>
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
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
This is joint work with Carlos Guestrin (University of Washington),
Ashish Kapoor (Microsoft Research) and Daniel Alexander (University
== 02/27/2013:David Mimno ==
== 03/13/2013: Dan Hopkins ==
== 03/27/2013: Richard Sproat ==
== 04/10/2013: Learning with Marginalized Corrupted Features ==
'''Speaker:''' [ http:// www. cse.wustl.edu/ ~kilian/ Kilian Weinberger], Washington University in St. Louis<br/>
'''Time:''' Wednesday, April 10, 2013, 11:00 AM<br/>
'''Venue:''' AVW 3258<br/>
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 ==
* [[CLIP Colloquium (Fall 2012)|Fall 2012]]
* [[CLIP Colloquium (Spring 2012)|Spring 2012]]
* [[CLIP Colloquium (Fall 2011)|Fall 2011]]
* [[CLIP Colloquium (Spring 2011)|Spring 2011]]
* [[CLIP Colloquium (Fall 2010)|Fall 2010]]