[Artificial Intelligence and Statistics Logo] Artificial Intelligence and Statistics 2010

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Conference Schedule

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
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