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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.
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| If you would like to get on the cl-colloquium@umiacs.umd.edu list or for other questions about the colloquium series, e-mail [mailto:jimmylin@umd.edu Jimmy Lin], the current organizer.
| | == CLIP Colloquium == |
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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 on Wednesday at 11AM online unless otherwise noted. Typically, external speakers have slots for one-on-one meetings with Maryland researchers. |
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| | If you would like to get on the clip-talks@umiacs.umd.edu list or for other questions about the colloquium series, e-mail [mailto:rudinger@umd.edu Rachel Rudinger], the current organizer. |
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| == 04/10/2013: Learning with Marginalized Corrupted Features ==
| | For up-to-date information, see the [https://talks.cs.umd.edu/lists/7 UMD CS Talks page]. (You can also subscribe to the calendar there.) |
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| '''Speaker:''' [http://www.cse.wustl.edu/~kilian/ Kilian Weinberger], Washington University in St. Louis<br/>
| | === Colloquium Recordings === |
| '''Time:''' Wednesday, April 10, 2013, 11:00 AM<br/>
| | * [[Colloqium Recording (Fall 2020)|Fall 2020]] |
| '''Venue:''' AVW 3258<br/>
| | * [[Colloqium Recording (Spring 2021)|Spring 2021]] |
| | * [[Colloqium Recording (Fall 2021)|Fall 2021]] |
| | * [[Colloqium Recording (Spring 2022)|Spring 2022]] |
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| 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.
| | === Previous Talks === |
| | * [[https://talks.cs.umd.edu/lists/7?range=past Past talks, 2013 - present]] |
| | * [[CLIP Colloquium (Spring 2012)|Spring 2012]] [[CLIP Colloquium (Fall 2011)|Fall 2011]] [[CLIP Colloquium (Spring 2011)|Spring 2011]] [[CLIP Colloquium (Fall 2010)|Fall 2010]] |
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| 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.
| | == CLIP NEWS == |
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| 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.
| | * News about CLIP researchers on the UMIACS website [http://www.umiacs.umd.edu/about-us/news] |
| | | * Please follow us on Twitter @ClipUmd[https://twitter.com/ClipUmd?lang=en] |
| '''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.
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| == Previous Talks ==
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| * [[CLIP Colloquium (Fall 2012)|Fall 2012]]
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| * [[CLIP Colloquium (Spring 2012)|Spring 2012]] | |
| * [[CLIP Colloquium (Fall 2011)|Fall 2011]]
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| * [[CLIP Colloquium (Spring 2011)|Spring 2011]]
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| * [[CLIP Colloquium (Fall 2010)|Fall 2010]]
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