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 |