Learning Workshop, Puerto Rico, March 19-22, 2007

PROGRAM

Monday, March 19
16:00 Registration
18:00 Dinner
19:30 Oral Session 1
19:30 Purnamrita Sarkar and Andrew Moore
A Tractable Approach to Finding Closest Truncated-hitting-time Neighbors in Large Graphs
[DjVu|PDF]
19:50 Frank DiMaio and Ameet Soni and Jude Shavlik and George Phillips
Guiding Particle Filtering with Marginal Approximations: an Application in Protein Image Interpretation
[DjVu|PDF]
20:10 Break
20:30 Omid Madani and Michael Connor
Ranked Recall: Efficient Classification by Learning Indices that Rank
[DjVu|PDF]
20:50 Marina Meila and Arthur Patterson and Jeff Bilmes
Consensus ranking under the exponential model
[DjVu|PDF]
Tuesday, March 20
08:00 Breakfast
09:00 Oral Session 2
09:00 Hugo Larochelle and Dumitru Erhan and Yoshua Bengio
Generalization to a zero-data task: an empirical study
[DjVu|PDF]
09:20 Ronan Collobert and Jason Weston
Fast Semantic Extraction Using a Novel Neural Network Architecture
[DjVu|PDF]
09:40 Risi Kondor
A complete set of rotationally and translationally invariant features for images
[DjVu|PDF]
10:00 Liberty.E and Almagor.M and Keller.Y and Coifman.R.R. and Zucker.S.W.
Scoring Psychological Questionnaires using Geometric Harmonics
[DjVu|PDF]
10:20 Break
10:40 Nicolas Le Roux and Pierre-Antoine Manzagol and Yoshua Bengio
Topmoumoute Online Natural Gradient Algorithm
[DjVu|PDF]
11:00 John C. Platt and Emre Kiciman and David A. Maltz
Tracking Time-Varying Hidden Faults using Stochastic Gradient Descent
[DjVu|PDF]
10:20 Jin Yu and Nicol N. Schraudolph and S.V.N. Vishwanathan
Online Limited-Memory Quasi-Newton Training of Support Vector Machines
[DjVu|PDF]
11:40 J. Andrew Bagnell and John Langford and Nathan Ratliff and David Silver
The Exponentiated Functional Gradient Algorithm for Structured Prediction Problems
[DjVu|PDF]
12:00 Lunch (on your own)
18:00 Dinner
19:30-22:00 Poster Session A
Wednesday, March 21
08:00 Breakfast
09:00 Oral Session 3
09:00 Atul Kanaujia, Cristian Sminchisescu, Dimitris Metaxas
Hierarchical Models for 3D Visual Inference
[DjVu|PDF]
09:20 Tony Jebara and Blake Shaw and Andrew Howard
Optimizing Eigen-Gaps and Spectral Functions using Iterated SDP
[DjVu|PDF]
09:40 Anthony Hoogs and Roderic Collins
Learning Hierarchical, Semantic Priors for Image Boundary Detection
[DjVu|PDF]
10:00 Ariadna Quattoni and Michael Collins and Trevor Darrell
Learning Visual Representations using Images with Captions
[DjVu|PDF]
10:20 Break
10:40 Xinhua Zhang and Douglas Aberdeen and S.V.N Vishwanathan
Conditional Random Fields for Reinforcement Learning
[DjVu|PDF]
11:00 Vikas C. Raykar and Ramani Duraiswami
Fast large scale Gaussian process regression using approximate matrix-vector products
[DjVu|PDF]
11:20 Alexander Gammerman and Vladimir Vovk
Conformal Predictors
[DjVu|PDF]
11:40 Yair Weiss and William T. Freeman
Learning Optimal Compressed Sensing
[DjVu|PDF]
12:00 Lunch (on your own)
18:00 Dinner
19:30-22:00 Poster Session B
Thursday, March 22
This is a joint AISTATS 2007 and Learning Workshop day. The oral and poster presentations and the banquet will be together with the Learning Workshop.
08:00 Breakfast
09:00 AI-Stats/Learning Oral Session
09:00 Yuhong Yang
How Powerful Can Any Regression Learning Procedure Be?
09:25 Bert Huang and Tony Jebara
Loopy Belief Propagation for Bipartite Maximum Weight b-Matching
09:50 Jiji Zhang
Generalized Do-Calculus with Testable Causal Assumptions
10:15 Break
10:45 Ruslan Salakhutdinov and Geoff Hinton
Deep Belief Networks
[DjVu|PDF]
11:25 Marc'Aurelio Ranzato, Y-Lan Boureau, Fu-Jie Huang, Yann LeCun
Unsupervised Learning of Sparse and Invariant Feature Hierarchies
[DjVu|PDF]
11:45 Lunch (on your own)
17:00-20:00 AI-Stats Poster Session 1
20:00 Banquet