[edit]
AISTATS Conference Schedule
Poster Session I, Thursday May 13
Contributed Posters Posters from Breaking-News AbstractsContributed 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 |