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


AISTATS Conference Schedule

Poster Session I, Thursday May 13

Contributed Posters Posters from Breaking-News Abstracts

Contributed Posters

Online Anomaly Detection under Adversarial Impact
M. Kloft and P. Laskov [abs] [pdf] [supplementary]

A Weighted Multi-Sequence Markov Model For Brain Lesion Segmentation
F. Forbes, S. Doyle, D. Garcia–Lorenzo, C. barillot and M. Dojat [abs] [pdf]

Online Passive-Aggressive Algorithms on a Budget
Z. Wang and S. Vucetic [abs] [pdf]

Guarantees for Approximate Incremental SVMs
N. Usunier, A. Bordes and L. Bottou [abs] [pdf]

Near-Optimal Evasion of Convex-Inducing Classifiers
B. Nelson, B. Rubinstein, L. Huang, A. Joseph, S. Lau, S. Lee, S. Rao, A. Tran and D. Tygar [abs] [pdf]

A Regularization Approach to Nonlinear Variable Selection
L. Rosasco, M. Santoro, S. Mosci, A. Verri and S. Villa [abs] [pdf]

Exploiting Covariate Similarity in Sparse Regression via the Pairwise Elastic Net
A. Lorbert, D. Eis, V. Kostina, D. Blei and P. Ramadge [abs] [pdf]

The Group Dantzig Selector
H. Liu, J. Zhang, X. Jiang and J. Liu [abs] [pdf]

Ultra-high Dimensional Multiple Output Learning With Simultaneous Orthogonal Matching Pursuit: Screening Approach
M. Kolar and E. Xing [abs] [pdf] [supplementary]

The Feature Selection Path in Kernel Methods
F. Li and C. Sminchisescu [abs] [pdf]

Exclusive Lasso for Multi-task Feature Selection
Y. Zhou, R. Jin and S. Chu–Hong Hoi [abs] [pdf]

Semi-Supervised Learning via Generalized Maximum Entropy
A. Erkan and Y. Altun [abs] [pdf]

Semi-Supervised Learning with Max-Margin Graph Cuts
B. Kveton, M. Valko, A. Rahimi and L. Huang [abs] [pdf]

Bayesian variable order Markov models
C. Dimitrakakis [abs] [pdf] [supplementary]

Bayesian Generalized Kernel Models
Z. Zhang, G. Dai, D. Wang and M. Jordan [abs] [pdf]

Mass Fatality Incident Identification based on nuclear DNA evidence
F. Corradi [abs] [pdf]

Approximate parameter inference in a stochastic reaction-diffusion model
A. Ruttor and M. Opper [abs] [pdf]

Parametric Herding
Y. Chen and M. Welling [abs] [pdf]

Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
M. Gutmann and A. Hyvärinen [abs] [pdf]

Understanding the difficulty of training deep feedforward neural networks
X. Glorot and Y. Bengio [abs] [pdf]

Exploiting Within-Clique Factorizations in Junction-Tree Algorithms
J. McAuley and T. Caetano [abs] [pdf]

Maximum-likelihood learning of cumulative distribution functions on graphs
J. Huang and N. Jojic [abs] [pdf]

Improving posterior marginal approximations in latent Gaussian models
B. Cseke and T. Heskes [abs] [pdf]

Nonparametric Tree Graphical Models
L. Song, A. Gretton and C. Guestrin [abs] [pdf] [supplementary]

Structured Sparse Principal Component Analysis
R. Jenatton, g. Obozinski and F. Bach [abs] [pdf]

Factorized Orthogonal Latent Spaces
M. Salzmann, C. Henrik Ek, R. Urtasun and T. Darrell [abs] [pdf]

Sufficient Dimension Reduction via Squared-loss Mutual Information Estimation
T. Suzuki and M. Sugiyama [abs] [pdf]

Identifying Cause and Effect on Discrete Data using Additive Noise Models
J. Peters, D. Janzing and B. Schoelkopf [abs] [pdf]

Using Descendants as Instrumental Variables for the Identification of Direct Causal Effects in Linear SEMs
H. Chan and M. Kuroki [abs] [pdf]

Learning Causal Structure from Overlapping Variable Sets
S. Triantafillou, I. Tsamardinos and I. Tollis [abs] [pdf]

Combining Experiments to Discover Linear Cyclic Models with Latent Variables
F. Eberhardt, P. Hoyer and R. Scheines [abs] [pdf]

Inference and Learning in Networks of Queues
C. Sutton and M. Jordan [abs] [pdf]

Deterministic Bayesian inference for the p* model
H. Austad and N. Friel [abs] [pdf]

Posters from Breaking-News Abstracts

Have I seen you before? Principles of Bayesian predictive classification revisited
J. Corander, Y. Cui, T. Koski and J. Siren

Decisive symmetric games: study of their decisiveness
F. Carreras, J. Freixas and M.A. Puente

A new variational Bayesian algorithm with Gaussian mixture component spitting for mining spatial data
B. Wu, C.A. McGrory and A.N. Pettitt

Variables selection in unsupervised classification by mixture using genotype data
W. Toussile

A convex regularization formulation for learning task relationships in multi-task learning
Y. Zhang and D.-Y. Yeung

Recursive modeling using xstatR
E.J. Harner and J. Tan

Adaptation and complexity regularisation in data streams
N.G. Pavlidis, D.K. Tasoulis, N.M. Adams and D.J. Hand

A general mixed-membership model with application to children's learning
A. Galyardt

Adaptive estimation for multivariate Gaussian data-streams: an approximately Bayesian approach
P. Rubin-Delanchy

Results of the active learning challenge
I. Guyon, G. Cawley, G. Dror and V, Lemaire

Learning why things change: the difference-based causality learner
M. Voortman, D. Dash and M.J. Druzdzel

A model-based method for transcription factor target identification from short gene expression time series data
A. Honkela, N.D. Lawrence and M. Rattray

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