NYU Lrning Wrkshp
Call for Abstract

NYU Workshop on Computational and Biological Learning

January 16th 2004, Warren Weaver Hall 1302

Workshop Program

NYU Workshop on Computational and Biological Learning
January 16th 2004, WarrenWeaver Hall 1302.

10:00 Introduction, Yann LeCun (Courant)
10:10 Ralph Grishman (Courant/Computer Science)
"Learning Methods for Information Extraction from Text" Home Page
10:30 Satoshi Sekine (Courant/Computer Science)
"Learning Methods in On-Demand Information Extraction" Home Page
10:40 Dan Melamed (Courant/Computer Science)
"Some Open Learning Problems in Computational Linguistics" Home Page
10:50 Cynthia Rudin, Ingrid Daubechie (Courant/Math), Rob Schapire (Princeton)
"The Dynamics of Boosting"
11:00-11:30 break
11:30 Foster Provost (Stern)
"Machine Learning for Suspicion Scoring: from Fraud Detection to Counterterrorism?" Abstract; Home Page
11:50: Denis Pelli (Psychology)
"How People Learn a New Alphabet". Home Page
12:10 Yann LeCun (Courant/Computer Science)
"Learning to Recognize Object Categories" Home Page
12:30-1:20 Lunch
1:20 Eero Simoncelli (CNS)
"learning optimal image decomopositions and neural response models" Home Page
1:40 Hannah Bayer, Paul Glimcher (CNS)
"Midbrain dopamine neurons encode a quantitative reward prediction error signal"
2:00 Claudia Perlich (Stern)
"Modeling in Complex Multi-Relational Domains"
2:10 Bill Greene (Stern)
"Simulation-based Estimation Methods in Econometrics". Home Page
2:40 Cliff Hurvich (Stern)
"Forecasting volatility of high frequency returns on the S&P 500" Home Page
3:00-3:30 Break
3:30 Souheil Inati, David Heeger (CNS)
"Advanced MRI Applications for Neuroscience"
3:50 Alex Vasilescu, Demetri Terzopoulos (Courant/Computer Science)
"TensorFaces" Home Page
4:10 Panos Mavromatis (Steinhardt/Music)
"Hidden Markov Models of Melodic Improvisation"
4:30 Wendy Suzuki (CNS)
"Associative learning signals in the medial temporal lobe"
4:50 Vasant Dhar (Stern)
"Genetic Search: Are simpler patterns more robust?" Home Page
5:00 Sofus Macskassy (Stern)
"The Relational Neighbor Classifier: Homophily in Action" Abstract; Home Page
5:10 Margaret Wright (Courant/CS)
Optimization and Learning, Concluding Remarks.
5:20-5:40 Open Discussion
5:40 Adjourn

General Information

Everyone is welcome to attend, even if you have not registered, but seating is limited to about 60 persons.

Lunch will be served at 12:30 for registered participants in the room next to WWH-1302 where the talks take place.

An laptop projector and an overhead projector will be available for speakers.

NYU Learning Mailing List

A mailing list for the NYU Learning community will be set up shortly. If you want to be on the inital list, please send email to yann AT cs.nyu.edu with the following information (please include [NYUWCBL] in the subject line):

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Call for Abstract

for information only, the deadline is passed

please disseminate this announcement within your group/lab/department/school

Dear Colleague,

We are glad to invite you to the first NYU Workshop on Computational and Biological Learning. The purpose of this workshop is to bring together people of the NYU community who are interested in learning, whether their primary interest is to apply learning techniques in their research, or whether their primary interest is to develop new theories, techniques, and tools for machine learning, computational models of biological learning, statistical estimation, and related fields were computational models are derived from data.

Learning and Statistical Estimation methods are used in a wide variety of domains spanning many academic disciplines at NYU: Computer Science, Neuroscience, Psychology, Statistics, Mathematics, Linguistics, Biology, Bioinformatics, Finance, Medicine, even Physics, Music Theory, and the Performing Arts. Current applications of learning methods include data mining, computer vision, natural language processing, behavior modeling, biological modeling, financial time-series prediction, weather prediction, DNA micro-array analysis, gene finding and biological sequence analysis, brain imaging, modeling improvised melodies, and many other topics.

Learning researchers are in search of interesting datasets to drive the development of new learning algorithms, while users of learning are in search of solutions to their data analysis and modeling problems. Therefore this workshop's primary goal will be to provide a forum in which researchers can exchange ideas and tips, advertise and exchange software tools, find help on specific topics, find interesting datasets to work with, and hopefully start new collaborations.

The workshop will be an informal get-together with short presentations (10 minutes), regular talks (20 minutes), and a small number of tutorial talks (30 minutes). A poster session (mostly for student projects) will also be held.

Talks may belong to the following (non-exhaustive) list of categories:

Application domains: Neuroscience, Psychology/Linguistics, Biology/Bio-informatics/Chemistry, Numerical Modeling/Physics, Computer Science/Artificial Intelligence, Finance/Economy, Statistics/Operations Research, Arts, Others.

Workshop Organization