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AISTATS Conference Schedule
Wednesday 12 May
| 16:00 - 19:00 | Registration |
| 19:30 - 21:30 | Dinner |
Thursday 13 May
| 07:45 - 08:30 | Breakfast |
| 08:30 - 08:45 | Welcome
Organizers |
| Invited Talk |
| 08:45 - 09:45 | Forensic Statistics: Where are We and Where are We Going?
Richard Gill |
| Network Models (Chair B. Schölkopf) |
| 09:45 - 10:10 | Boosted optimization for network classification
T. Hancock and H. Mamitsuka [abs] [pdf] |
| 10:10 - 10:35 | Detecting weak but hierarchically-structured patterns in networks
A. Singh and R. Nowak [abs] [pdf] |
| 10:35 - 13:00 | Coffee Break and Poster Session I |
| 13:00 - 17:00 | Lunch |
| Statistical Learning Theory (Chair Y. Altun) |
| 17:00 - 17:25 | Risk bounds for transduction and semi-supervised learning relative to data structure
G. Lever [abs] [pdf] [supplementary] |
| 17:25 - 17:50 | Multiclass-multilabel classification with more labels than examples
O. Dekel and O. Shamir [abs] [pdf] |
| 17:50 - 18:15 | Empirical Bernstein boosting
P. Shivaswamy and T. Jebara [abs] [pdf] |
| 18:15 - 18:45 | Tea Break |
| Bayesian nonparametrics and causal inference (Chair S. Petrone) |
| 18:45 - 19:10 | Sufficient covariates and linear propensity analysis
H. Guo and P. Dawid [abs] [pdf] |
| 19:10 - 19:35 | Dirichlet process mixtures of generalised linear models
L. Hannah, D. Blei and W. Powell [abs] [pdf] |
| 19:35 - 20:00 | Bayesian Gaussian process latent variable model
M. Titsias and N. Lawrence [abs] [pdf] |
| 20:00 - 22:00 | Conference Banquet |
Friday 14 May
| 07:45 - 08:45 | Breakfast |
| Invited Talk |
| 08:45 - 09:45 | Approximate Bayesian Computation: What, Why and How?
Simon Tavaré |
| Deep Learning (Chair Y. Bengio) |
| 09:45 - 10:10 | Factored 3-way restricted Boltzmann machines for modeling natural images
M. Ranzato, A. Krizhevsky and G. Hinton [abs] [pdf] |
| 10:10 - 10:35 | Learning the structure of deep sparse graphical models
R. Adams, H. Wallach and Z. Ghahramani [abs] [pdf] [supplementary] |
| 10:35 - 13:00 | Coffee Break and Poster Session II |
| 13:00 - 17:00 | Lunch |
| Approximate Inference (Chair A. Globerson) |
| 17:00 - 17:25 | Solving the uncapacitated facility location problem using message passing problems
N. Lazic, B. Frey and P. Arabi [abs] [pdf] |
| 17:25 - 17:50 | Dense message passing for sparse principal component analysis
K. Sharp and M. Rattray [abs] [pdf] |
| 17:50 - 18:15 | Focused belief propagation for query-specific inference
A. Chechetka and C. Guestrin [abs] [pdf] |
| 18:15 - 18:45 | Tea Break |
| Online Learning, Control & Information Theory (Chair A. Singh) |
| 18:45 - 19:10 | Exploiting feature covariance in high-dimensional online learning
J. Ma, A. Kulesza, M. Dredze, K. Crammer, L. Saul and F. Pereira [abs] [pdf] |
| 19:10 - 19:35 | REGO: Rank-based estimation of Renyi information using Euclidean graph optimization
B. Poczos, C. Szepesvari and S. Kirshner [abs] [pdf] |
| 19:35 - 20:00 | Coherent inference on optimal play in game trees
P. Hennig, D. Stern and T. Graepel [abs] [pdf] |
| 20:00 - 22:00 | Dinner |
Saturday 15 May
| 07:45 - 08:45 | Breakfast |
| Invited Talk |
| 08:45 - 09:45 | Nonparametric Learning of Functions and Graphs in High Dimensions
John Lafferty |
| Kernel Methods (Chair A. Gretton) |
| 09:45 - 10:10 | Nonlinear functional regression: a functional RKHS approach
H. Kadri [abs] [pdf] |
| 10:10 - 10:35 | On the relation between universality, characteristic kernels and RKHS embedding of measures
B. Sriperumbudur, K. Fukumizu and G. Lanckreit [abs] [pdf] |
| 10:35 - 13:00 | Coffee Break and Poster Session III |
| 13:00 - 17:00 | Lunch |
| Graphical Models and Causal Inference (Chair I. Murray) |
| 17:00 - 17:25 | On combining graph-based variance reduction schemes
V. Gogate and R. Dechter [abs] [pdf] |
| 17:25 - 17:50 | Convex structure learning in log-linear models beyond pairwise potentials
M. Schmidt and K. Murphy [abs] [pdf] |
| 17:50 - 18:15 | Modeling annotator expertise: learning when everybody knows a bit of something
R. Rosales, Y. Yan, G. Fung and J. Dy [abs] [pdf] |
| 18:15 - 18:45 | Tea Break |
| Low-rank Methods & Information Retrieval (Chair M. Niranjan) |
| 18:45 - 19:10 | Fluid dynamics models for low rank discriminant analysis
Y.-K. Noh, B.-T. Zhang and D. Lee [abs] [pdf] |
| 19:10 - 19:35 | Reduced-rank hidden Markov models
S. Siddiqi, B. Boots and G. Gordon [abs] [pdf] |
| 19:35 - 20:00 | Half transductive ranking
B. Bai, J. Weston, D. Grangier, R. Collobert, C. Cortes and M. Mohri [abs] [pdf] |
| 20:00 - 22:00 | Dinner |