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5th Annual Machine Learning Symposium

5th Annual Machine Learning Symposium

Friday, October 22, 2010

The New York Academy of Sciences

Presented By


This is the fifth symposium on Machine Learning at the New York Academy of Sciences. The aim of these series of symposia is to build a community of scientists in machine learning from the NYC area's academic, government, and industrial institutions by convening and promoting the exchange of ideas in a neutral setting.


Gold Sponsor

  • IBM Research

Silver Sponsors

  • Springer Science+ Business Media
  • Yahoo Research
  • Google Research

Past Machine Learning Symposia


*Presentation times are subject to change.

9:30 AM

Coffee & Poster Set-Up

10:00 AM

Opening Remarks

10:10 AM

Probabilistic Topic Models
David Blei, PhD, Princeton University

10:55 AM

Student Talks

11:25 AM

Learning Hierarchies of Features
Yann LeCun, PhD, New York University

12:10 PM


1:20 PM

Student Talks

1:50 PM

Dual Decomposition and Linear Programming Relaxations for Inference in Natural Language Processing 
Michael Collins, PhD, Massachusetts Institute of Technology

2:35 PM

Poster Session

3:45 PM

Cluster Trees, Near-neighbor Graphs, and Continuum Percolation
Sanjoy Dasgupta, PhD, University of California

4:30 PM

Student Award Winner Announcement & Closing Remarks

4:45 PM

End of program

5:00 PM

Optional Science Alliance meeting for students



David Blei, PhD

Princeton University

David Blei is an assistant professor of Computer Science at Princeton University. He received his PhD in 2004 at U.C. Berkeley and was a postdoctoral fellow at Carnegie Mellon University. His research focuses on probabilistic models, Bayesian nonparametric methods, and approximate posterior inference. He works on a variety of applications, including text, images, music, social networks, and scientific data.

Sanjoy Dasgupta, PhD

University of California

Sanjoy Dasgupta is an Associate Professor in the Department of Computer Science and Engineering at UC San Diego. Prior to joining UCSD in 2002, he was a senior member of the technical staff at AT&T Labs—Research. He obtained a Ph.D. in Computer Science from UC Berkeley in 2000, and a B.A. in Computer Science from Harvard in 1993. His research area is learning theory, with a focus on unsupervised and minimally supervised learning.

Michael Collins, PhD

Massachusetts Institute of Technology

Michael Collins is an associate professor of computer science at MIT. He received a PhD in computer science from the University of Pennsylvania in 1998. Prior to joining MIT in 2003, he was a researcher at AT&T Labs-Research. His research has focused on topics including statistical parsing, structured prediction problems in machine learning, and NLP applications including machine translation, dialog systems, and speech recognition.

Yann LeCun, PhD

New York University

Yann LeCun is Silver Professor of Computer Science and Neural Science at the Courant Institute of Mathematical Sciences and at the Center for Neural Science of New York University. He received an Electrical Engineer Diploma from Ecole Supérieure d'Ingénieurs en Electrotechnique et Electronique (ESIEE), Paris in 1983, and a PhD in Computer Science from Université Pierre et Marie Curie (Paris) in 1987. After a postdoc at the University of Toronto, he joined AT&T Bell Laboratories in Holmdel, NJ, in 1988. He became head of the Image Processing Research Department at AT&T Labs-Research in 1996, and joined NYU in 2003, after a brief period as Fellow at the NEC Research Institute in Princeton. His current interests include machine learning, computer vision, pattern recognition, mobile robotics, and computational neuroscience. He has published over 150 technical papers on these topics as well as on neural networks, handwriting recognition, image processing and compression, and VLSI design. His handwriting recognition technology is used by several banks around the world to read checks. His image compression technology, called DjVu, is used by hundreds of web sites and publishers and millions of users to distribute and access scanned documents on the Web, and his image recognition technique, called Convolutional Network, has been deployed by companies such as Google, Microsoft, NEC, France Telecom and several startup companies for document recognition, human-computer interaction, image indexing, and video analytics. He has been on the editorial board of IJCV, IEEE PAMI, IEEE Trans on Neural Networks, was program chair of CVPR'06, and is chair of the annual Learning Workshop. He is on the science advisory board of Institute for Pure and Applied Mathematics, and is the co-founder of MuseAmi, a music technology company.

Organizing Committee

Naoki Abe, PhD


Corinna Cortes, PhD

Google, Inc.

Tony Jebara, PhD

Columbia University

Michael Kearns, PhD

University of Pennsylvania

John Langford, PhD

Yahoo Research

Mehryar Mohri, PhD

New York University

Robert Schapire, PhD

Princeton University

David Waltz, PhD

Columbia University


Gold Sponsor

  • IBM Research

Silver Sponsors

  • Springer Science+ Business Media
  • Yahoo Research
  • Google Research


Probabilistic Topic Models

David Blei, PhD, Princeton University

Probabilistic topic modeling provides an important suite of tools for the unsupervised analysis of large collections of documents. Topic modeling algorithms can uncover the underlying themes of a collection and decompose its documents according to those themes. This analysis can then be used for tasks like corpus exploration, document search, and a variety of prediction problems. In this talk, I will review the state-of-the-art in probabilistic topic models and describe two recent innovations.

First, I will describe a topic model developed for analyzing political texts, such as bills and laws. With this model, we can characterize the political tone of a government body and make predictions about how its members will vote on new legislation.

Second, I will describe an on-line strategy for fitting topic models.Rather than analyzing a corpus in batch, our algorithm can analyze documents arriving in a stream. An analysis of 3.3M articles from Wikipedia shows that this on-line approach fits topic models that are as good or better than those found with the traditional batch approach, and fits them in a fraction of the time.

Learning Hierarchies of Features

Yann LeCun, PhD, New York University

Intelligent perceptual tasks such as vision and audition require the construction of good internal representations. Theoretical and empirical evidence suggest that the perceptual world is best represented by a multi-stage hierarchy in which features in successive stages are increasingly global, invariant, and abstract. An important challenge for Machine Learning is to devise "deep learning" methods than can automatically learn good feature hierarchies from labeled and unlabeled data. A class of such methods that combine unsupervised sparse coding, and supervised refinement will be described. A number of applications will be shown through videos and live demos, including a category-level object recognition system that can be trained on line, and a trainable vision system for off-road mobile robot.

Cluster trees, Near-neighbor Graphs, and Continuum Percolation

Sanjoy Dasgupta, PhD, MUniversity of California, San Diego

What information does the clustering of a finite data set reveal about the underlying distribution from which the data were sampled? This basic question has proved elusive even for the most widely-used clustering procedures. A natural criterion is to seek clusters that converge (as the data set grows) to regions of high density. When all possible density levels are considered, this is a hierarchical clustering problem where the sought limit is called the “cluster tree”. We give a simple three-line algorithm for estimating this tree that implicitly constructs a multiscale hierarchy of near-neighbor graphs on the data points. We show that the procedure is consistent, answering a long-standing open problem of Hartigan. We also obtain rates of convergence, using a percolation argument that gives insight into how near-neighbor graphs should be constructed.

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