Learning Workshop, Snowbird, April 1-4, 2008

PROGRAM

Tuesday, April 1
16:00 Registration
18:00 Dinner
19:00 Oral Session 1
19:00 Simon Lacoste-Julien and Fei Sha and Michael I. Jordan
Conditionally Trained Latent Dirichlet Allocation for Text Modeling and Categorization
[DjVu|PDF]
19:20 Ryan Prescott Adams and David J.C. MacKay
The Gaussian Process Density Sampler
[DjVu|PDF]
19:40 C. Lee Giles and Prasenjit Mitra and Karl Mueller and James Z. Wang and Bingjun Sun and Levent Bolelli and Xiaonan Lu and Ying Liu and Isaac Councill and William Brower and Qingzhao Tan and Anuj Jaiswal and James Kubicki and Barbara Garrison and Joel Bandstra and Juan Pablo Fernandez Ramirez
ChemXSeer: A Web Search Engine and Repository for e-Chemistry
[DjVu|PDF]
20:00 Break
20:20 Jacob Abernethy and Francis Bach and Theodoros Evgeniou and Jean-Philippe Vert
{A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization
[DjVu|PDF]
20:40 Nathan Intrator
Novel Interpretation for EEG Data and Prediction of Epileptic Seizure
[DjVu|PDF]
Wednesday, April 2
07:00 Breakfast
08:00 Oral Session 2
08:00 Dean P. Foster and Lyle H. Ungar
Maximal Subset Feature Selection for BioInformatics
[DjVu|PDF]
08:20 Patrick Haffner
A Primal/Dual Stump Algorithm for Large Numerical Datasets
[DjVu|PDF]
08:40 Ronan Collobert and Jason Weston
Natural Language Processing: The Brain Way
[DjVu|PDF]
09:20 Break
09:40 Dan Klein
TBA
[DjVu|PDF]
10:20 Sumit Chopra and Trivikaraman Thampy and John Leahy and Andrew Caplin and Yann LeCun
Factor Graphs for Relational Regression
[DjVu|PDF]
10:40 Piyush Rai and Hal Daume III
Non-parametric Bayesian Hierarchical Factor Modeling and Regression
[DjVu|PDF]
11:00 Rich Caruana, Nikos Karampatziakis, Ainur Yessenalina
Learning in High Dimensions
[DjVu|PDF]
11:20 Lunch, Golden Cliff
17:30 Dinner, Golden Cliff
19:00-22:00 Poster Session A
Thursday, April 3
07:00 Breakfast
08:00 Oral Session 2
08:00 Thomas M. Breuel
Distributed Classifier Training for Large Scale OCR
[DjVu|PDF]
08:20 Oliver Williams and Andrew Fitzgibbon
Optimizing Matrix Computations for Learning
[DjVu|PDF]
08:40 Denis G. Pelli and Katharine A. Tillman
The uncrowded window for object recognition
[DjVu|PDF]
09:20 Break
09:40 Rob Fergus
Large image databases and small codes for object recognition
[DjVu|PDF]
10:20 Pascal Vincent and Hugo Larochelle and Yoshua Bengio and Pierre-Antoine Manzagol
Deep Learning with Denoising Autoencoders
[DjVu|PDF]
10:40 Thomas Dean
Learning to Parse Video into Stable Spatiotemporal Volumes
[DjVu|PDF]
11:00 James Bergstra and Yoshua Bengio and Jerome Louradour
Image Classification using Higher-Order Neural Models
[DjVu|PDF]
11:20 Lunch, Golden Cliff
17:30 Dinner, Golden Cliff
19:00-22:00 Poster Session B
Friday, April 4
07:00 Breakfast
08:00 Oral Session 2
08:00 Alon Orlitsky
TBA
[DjVu|PDF]
08:40 Ted Sandler, John Blitzer and Lyle H. Ungar
Learning with Locally Linear Feature Regularization
[DjVu|PDF]
09:00 Pradeep Ravikumar and Martin Wainwright and Bin Yu
Single Index Convex Experts: Efficient Estimation via Adapted Bregman Losses
[DjVu|PDF]
09:20 Break
09:40 Andrew Y. Ng, Stephen Gould, Morgan Quigley, Ashutosh Saxena and Eric Berger
STAIR: The STanford Artificial Intelligence Robot project
[DjVu|PDF]
10:20 Jennifer Listgarten and David Heckerman
Determining the Number of Non-Spurious Arcs in a Learned DAG
[DjVu|PDF]
10:40 Dan Roth and Kevin Small
Active Learning for Pipeline Models
[DjVu|PDF]
11:00 Javier R. Movellan
Minimizing Probability Currents: A General Framework for Learning
[DjVu|PDF]
11:20 Lunch, Golden Cliff
17:30 Banquet, Golden Cliff
19:30 Raia Hadsell and Pierre Sermanet and Ayse Erkan and Koray Kavukcuoglu and Urs Muller and Yann LeCun
Autonomous Learning for Long Range Vision in Mobile Robots
[DjVu|PDF]
19:50 Christopher Raphael
Musical Accompaniment Systems
[DjVu|PDF]
20:10 Sung-Hee Lee and Demetri Terzopoulos
Learning Neuromuscular Control for the Biomechanical Simulation of the Neck-Head-Face Complex
[DjVu|PDF]