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AISTATS Conference Schedule
Poster Session II, Friday May 14
Contributed PostersPosters from Breaking-News Abstracts
Contributed Posters
On the Impact of Kernel Approximation on Learning Accuracy
C. Cortes, M. Mohri and A. Talwalkar [abs] [pdf] |
Kernel Partial Least Squares is Universally Consistent
G. Blanchard and N. Krämer [abs] [pdf] |
Risk Bounds for Levy Processes in the PAC-Learning Framework
C. Zhang and D. Tao [abs] [pdf] |
Negative Results for Active Learning with Convex Losses
S. Hanneke and L. Yang [abs] [pdf] |
Impossibility Theorems for Domain Adaptation
S. Ben David, T. Lu, T. Luu and D. Pal [abs] [pdf] |
Bayesian Online Learning for Multi-label and Multi-variate Performance Measures
X. Zhang, T. Graepel and R. Herbrich [abs] [pdf] |
Infinite Predictor Subspace Models for Multitask Learning
P. Rai and H. Daume III [abs] [pdf] |
Neural conditional random fields
T. Do and T. Artieres [abs] [pdf] |
Structured Prediction Cascades
D. Weiss and B. Taskar [abs] [pdf] |
Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity
S. Kakade, O. Shamir, K. Sindharan and A. Tewari [abs] [pdf] [supplementary] |
Feature Selection using Multiple Streams
P. Dhillon, D. Foster and L. Ungar [abs] [pdf] |
Fast Active-set-type Algorithms for L1-regularized Linear Regression
J. Kim and H. Park [abs] [pdf] |
Nonparametric prior for adaptive sparsity
V. Raykar and L. Zhao [abs] [pdf] |
Elliptical slice sampling
I. Murray, R. Adams and D. MacKay [abs] [pdf] [supplementary] |
Multi-Task Learning using Generalized t Process
Y. Zhang and D. Yeung [abs] [pdf] [supplementary] |
Efficient Multioutput Gaussian Processes through Variational Inducing Kernels
M. Alvarez, D. Luengo, M. Titsias and N. Lawrence [abs] [pdf] |
Gaussian processes with monotonicity information
J. Riihimäki and A. Vehtari [abs] [pdf] |
On the Convergence Properties of Contrastive Divergence
I. Sutskever and T. Tieleman [abs] [pdf] |
Tempered Markov Chain Monte Carlo for training of Restricted Boltzmann Machines
G. Desjardins, A. Courville, Y. Bengio, P. Vincent and O. Delalleau [abs] [pdf] |
Learning with Blocks: Composite Likelihood and Contrastive Divergence
A. Asuncion, Q. Liu, A. Ihler and P. Smyth [abs] [pdf] |
Polynomial-Time Exact Inference in NP-Hard Binary MRFs via Reweighted Perfect Matching
N. Schraudolph [abs] [pdf] |
HOP-MAP: Efficient Message Passing with High Order Potentials
D. Tarlow, I. Givoni and R. Zemel [abs] [pdf] |
Why are DBNs sparse?
S. Chatterjee and S. Russell [abs] [pdf] |
Graphical Gaussian modelling of multivariate time series with latent variables
M. Eichler [abs] [pdf] |
Approximation of hidden Markov models by mixtures of experts with application to particle filtering
J. Olsson and J. Ströjby [abs] [pdf] |
Learning Nonlinear Dynamic Models from Non-sequenced Data
T. Huang, L. Song and J. Schneider [abs] [pdf] |
Nonparametric Bayesian Matrix Factorization by Power-EP
N. Ding, Y. Qi, R. Xiang, I. Molloy and N. Li [abs] [pdf] |
Collaborative Filtering on a Budget
A. Karatzoglou, A. Smola and M. Weimer [abs] [pdf] |
Collaborative Filtering via Rating Concentration
B. Huang and T. Jebara [abs] [pdf] |
Descent Methods for Tuning Parameter Refinement
A. Lorbert and P. Ramadge [abs] [pdf] |
Learning Policy Improvements with Path Integrals
E. Theodorou, J. Buchli and S. Schaal [abs] [pdf] |
Efficient Reductions for Imitation Learning
S. Ross and D. Bagnell [abs] [pdf] [supplementary] |
A Markov-Chain Monte Carlo Approach to Simultaneous Localization and Mapping
P. Torma, A. György and C. Szepesvári [abs] [pdf] |
Incremental Sparsification for Real-time Online Model Learning
D. Nguyen–Tuong and J. Peters [abs] [pdf] |
Posters from Breaking-News Abstracts
On predictive distributions in fuzzy Bayesian inference
R. Viertl |
Proximal methods for sparse hierarchical dictionary learning
R. Jenatton, J. Mairal, G. Obozinski and F. Bach |
Estimation in the Burr X parameters and reliability using lower records
A. Bazlizi |
Web scale image annotation: learning to rank with joint word-image embeddings
J. Weston, S. Bengio and N. Usunier |
Unlearning for better mixing
O. Breuleux, Y. Bengio and P. Vincent |
TREERANK: a statistical software for bipartite ranking
N. Basklotis, S. Clemencon, M. Depecker and N. Vayatis |
Online semi-supervised learning on quantized graphs
M. Valko, B. Kveton and L. Huang |
Enhancing the efficieny of spectral clustering with partial supervision
D. Mavroeidis |
Non-parametric change detection using sequential kernel density estimation
G.J. Ross, D.K. Tasoulis and N.M. Adams |
Finding the MAP configuration of a subset of latent variables in a graphical model with discrete variables
G. Teodoru, C. Blundell and M. Sahani |
A statistical test to detect distance concentration from data sets
A. Kaban |