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---
title: Poster Session
layout: default
---
<h1>AISTATS*2012 Poster Sessions</h1>
<p>There are three poster sessions: <a href="#Poster1">Poster Session I</a>, <a href="#Poster2">Poster Session II</a>, and <a href="#Poster3">Poster Session III</a>. Each poster has a poster number. <b>Note that posters with nearby numbers should be located to each other in the conference hall, and that the poster board is two meters high one meter wide.</b></p>
<h2><a name="Poster1" id="Poster1"></a>Poster Session I (Saturday 21 April) </h2>
<p>Subjects roughly include Structured outputs, multitask, deep learning, bandits, clustering, decision processes, text, active learning, computational biology, low rank models and matrix completion, and speech. </p>
<p> Posters with nearby numbers should be located to each other in the conference hall. Note that the poster board is two meters high one meter wide.</p>
<h3> Contributed Posters </h3>
<table border="1">
<tr>
<th width="8%" scope="col"><div align="center">Poster number </div></th>
<th scope="col" align="center">Paper title<br />
<em>Authors</em>
</th>
</tr>
<tr>
<td width="8%" height="17" align="right"><div align="center">34</div></td>
<td>Contextual Bandit Learning with Predictable Rewards
<br />
<em>Alekh Agarwal, Miroslav Dudik, Satyen Kale, John Langford and Robert Schapire</em>
</td>
</tr>
<tr height="17">
<td width="8%" height="17" align="right"><div align="center">44</div></td>
<td>History-alignment models for bias-aware prediction of virological response to HIV combination therapy <br />
<em>Jasmina Bogojeska, Daniel Stöckel, Maurizio Zazzi, Rolf Kaiser, Francesca Incardona, Michal Rosen-Zvi and Thomas Lengauer </em></td>
</tr>
<tr height="17">
<td width="8%" height="17" align="right"><div align="center">42</div></td>
<td>Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing <br />
<em>Antoine Bordes, Xavier Glorot, Jason Weston and Yoshua Bengio</em></td>
</tr>
<tr height="17">
<td width="8%" height="17" align="right"><div align="center">36</div></td>
<td>Optimistic planning for Markov decision processes <br />
<em>Lucian Busoniu and Remi Munos </em></td>
</tr>
<tr height="17">
<td width="8%" height="17" align="right"><div align="center">31</div></td>
<td>Bandit Theory meets Compressed Sensing for high dimensional Stochastic Linear Bandit <br />
<em>Alexandra Carpentier and Remi Munos</em></td>
</tr>
<tr height="17">
<td width="8%" height="17" align="right"><div align="center">38</div></td>
<td>Hierarchical Relative Entropy Policy Search <br />
<em>Christian Daniel, Gerhard Neumann and Jan Peters </em></td>
</tr>
<tr height="17">
<td width="8%" height="17" align="right"><div align="center">13</div></td>
<td>Deterministic Annealing for Semi-Supervised Structured Output Learning <br />
<em>Paramveer Dhillon, Sathiya Keerthi, Kedar Bellare, Olivier Chapelle and Sundararajan Sellamanickam</em></td>
</tr>
<tr height="17">
<td width="8%" height="17" align="right"><div align="center">40</div></td>
<td>UPAL: Unbiased Pool Based Active Learning <br />
<em>Ravi Ganti and Alexander Gray </em></td>
</tr>
<tr height="17">
<td width="8%" height="17" align="right"><div align="center">18</div></td>
<td>Scalable Inference on Kingman's Coalescent using Pair Similarity <br />
<em>Dilan Gorur, Levi Boyles and Max Welling</em></td>
</tr>
<tr height="17">
<td width="8%" height="17" align="right"><div align="center">37</div></td>
<td>On Average Reward Policy Evaluation in Infinite-State Partially Observable Systems <br />
<em>Yuri Grinberg and Doina Precup </em></td>
</tr>
<tr height="17">
<td width="8%" height="17" align="right"><div align="center">19</div></td>
<td>Information Theoretic Model Validation for Spectral Clustering <br />
<em>Morteza Haghir Chehreghani, Alberto Giovanni Busetto and Joachim M. Buhmann</em></td>
</tr>
<tr height="17">
<td width="8%" height="17" align="right"><div align="center">30</div></td>
<td>Stochastic Bandit Based on Empirical Moments <br />
<em>Junya Honda and Akimichi Takemura </em></td>
</tr>
<tr height="17">
<td width="8%" height="17" align="right"><div align="center">33</div></td>
<td>On Bayesian Upper Confidence Bounds for Bandit Problems <br />
<em>Emilie Kaufmann, Olivier Cappé and Aurélien Garivier </em></td>
</tr>
<tr height="17">
<td width="8%" height="17" align="right"><div align="center">23</div></td>
<td>Online Clustering of Processes <br />
<em>Azadeh Khaleghi, Daniil Ryabko, Jeremie Mary and Philippe Preux </em></td>
</tr>
<tr height="17">
<td width="8%" height="17" align="right"><div align="center">11</div></td>
<td>Joint Estimation of Structured Sparsity and Output Structure in Multiple-Output Regression via Inverse-Covariance Regularization <br />
<em>Kyung-Ah Sohn and Seyoung Kim </em></td>
</tr>
<tr height="17">
<td width="8%" height="17" align="right"><div align="center">26</div></td>
<td>Multiple Texture Boltzmann Machines <br />
<em>Jyri Kivinen and Christopher Williams </em></td>
</tr>
<tr height="17">
<td width="8%" height="17" align="right"><div align="center">47</div></td>
<td>Bayesian Group Factor Analysis <br />
<em>Seppo Virtanen, Arto Klami, Suleiman Khan and Samuel Kaski </em></td>
</tr>
<tr height="17">
<td width="8%" height="17" align="right"><div align="center">7</div></td>
<td>Bayesian Comparison of Machine Learning Algorithms on Single and Multiple Datasets <br />
<em>Alexandre Lacoste, Francois Laviolette and Mario Marchand </em></td>
</tr>
<tr height="17">
<td width="8%" height="17" align="right"><div align="center">20</div></td>
<td>Efficient Hypergraph Clustering <br />
<em>Marius Leordeanu and Cristian Sminchisescu </em></td>
</tr>
<tr height="17">
<td width="8%" height="17" align="right"><div align="center">25</div></td>
<td>Deep Boltzmann Machines as Feed-Forward Hierarchies <br />
<em>Grégoire Montavon, Mikio Braun and Klaus-Robert Müller </em></td>
</tr>
<tr height="17">
<td width="8%" height="17" align="right"><div align="center">48</div></td>
<td>High-Rank Matrix Completion <br />
<em>Brian Eriksson, Laura Balzano and Robert Nowak </em></td>
</tr>
<tr height="17">
<td width="8%" height="17" align="right"><div align="center">9</div></td>
<td>Part & Clamp: Efficient Structured Output Learning <br />
<em>Patrick Pletscher and Cheng Soon Ong</em></td>
</tr>
<tr height="17">
<td width="8%" height="17" align="right"><div align="center">12</div></td>
<td>Learning Low-order Models for Enforcing High-order Statistics <br />
<em>Patrick Pletscher and Pushmeet Kohli</em></td>
</tr>
<tr height="17">
<td width="8%" height="17" align="right"><div align="center">14</div></td>
<td>Exploiting Unrelated Tasks in Multi-Task Learning <br />
<em>bernardino Romera Paredes, Andreas Argyriou, Nadia Berthouze and Massimiliano Pontil </em></td>
</tr>
<tr height="17">
<td width="8%" height="17" align="right"><div align="center">24</div></td>
<td>Deep Learning Made Easier by Linear Transformations in Perceptrons <br />
<em>Tapani Raiko, Harri Valpola and Yann LeCun </em></td>
</tr>
<tr height="17">
<td width="8%" height="17" align="right"><div align="center">35</div></td>
<td>No Internal Regret via Neighborhood Watch <br />
<em>Dean Foster and Alexander Rakhlin </em></td>
</tr>
<tr height="17">
<td width="8%" height="17" align="right"><div align="center">21</div></td>
<td>Constrained 1-Spectral Clustering <br />
<em>Syama Sundar Rangapuram and Matthias Hein </em></td>
</tr>
<tr height="17">
<td width="8%" height="17" align="right"><div align="center">17</div></td>
<td>Active Learning from Multiple Knowledge Sources <br />
<em>Yan Yan, Romer Rosales, Glenn Fung, Faisal Farooq, Bharat Rao and Jennifer Dy </em></td>
</tr>
<tr height="17">
<td width="8%" height="17" align="right"><div align="center">43</div></td>
<td>A Two-Graph Guided Multi-task Lasso Approach for eQTL Mapping <br />
<em>Xiaohui Chen, Xinghua Shi, Xing Xu, Zhiyong Wang, Ryan Mills, Charles Lee and jinbo Xu </em></td>
</tr>
<tr height="17">
<td width="8%" height="17" align="right"><div align="center">32</div></td>
<td>Multi-armed Bandit Problems with History <br />
<em>Pannagadatta Shivaswamy and Thorsten Joachims </em></td>
</tr>
<tr height="17">
<td width="8%" height="17" align="right"><div align="center">28</div></td>
<td>Flexible Martingale Priors for Deep Hierarchies <br />
<em>Jacob Steinhardt and Zoubin Ghahramani </em></td>
</tr>
<tr height="17">
<td width="8%" height="17" align="right"><div align="center">22</div></td>
<td>Consistency and Rates for Clustering with DBSCAN <br />
<em>Bharath Sriperumbudur and Ingo Steinwart </em></td>
</tr>
<tr height="17">
<td width="8%" height="17" align="right"><div align="center">45</div></td>
<td>Scalable Personalization of Long-Term Physiological Monitoring: Active Learning Methodologies for Epileptic Seizure Onset Detection <br />
<em>Guha Balakrishnan and Zeeshan Syed </em></td>
</tr>
<tr height="17">
<td width="8%" height="17" align="right"><div align="center">29</div></td>
<td>Multiresolution Deep Belief Networks <br />
<em>Yichuan Tang and Abdel-rahman Mohamed </em></td>
</tr>
<tr height="17">
<td width="8%" height="17" align="right"><div align="center">10</div></td>
<td>Structured Output Learning with High Order Loss Functions <br />
<em>Daniel Tarlow and Richard Zemel </em></td>
</tr>
<tr height="17">
<td width="8%" height="17" align="right"><div align="center">27</div></td>
<td>Krylov Subspace Descent for Deep Learning <br />
<em>Oriol Vinyals and Daniel Povey </em></td>
</tr>
<tr height="17">
<td width="8%" height="17" align="right"><div align="center">8</div></td>
<td>Robust Multi-task Regression with Grossly Corrupted Observations <br />
<em>Huan Xu and Chenlei Leng </em></td>
</tr>
<tr height="17">
<td width="8%" height="17" align="right"><div align="center">16</div></td>
<td>A Composite Likelihood View for Multi-Label Classification <br />
<em>Yi Zhang and Jeff Schneider </em></td>
</tr>
<tr height="17">
<td width="8%" height="17" align="right"><div align="center">49</div></td>
<td>Beta-Negative Binomial Process and Poisson Factor Analysis <br />
<em>Mingyuan Zhou, Lauren Hannah, David Dunson and Lawrence Carin </em></td>
</tr>
<tr height="17">
<td width="8%" height="17" align="right"><div align="center">15</div></td>
<td>Multi-label Subspace Ensemble <br />
<em>Tianyi Zhou and Dacheng Tao </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">50</div></td>
<td>Infinite-Dimensional Kalman Filtering Approach to Spatio-Temporal Gaussian Process Regression <br />
<em>Simo Särkkä and Jouni Hartikainen </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">46</div></td>
<td>Message-Passing Algorithms for MAP Estimation Using DC Programming <br />
<em>Akshat Kumar, Shlomo Zilberstein and Marc Toussaint </em></td>
</tr>
</table>
<h3><!-- Breaking news abstracts-->Posters from Breaking-News Abstracts </h3>
<table border="1">
<tr>
<th width="6%" scope="col"><div align="center">Poster number </div></th>
<th scope="col" align="center">Paper title<br />
<em>Authors</em></th>
</tr>
<tr>
<td align="right" width="81"><div align="center">1</div></td>
<td width="614">The effect of subsample size in Stability Selection <br />
<em>Gilles Blanchard, Andre Beinrucker and Urun Dogan</em></td>
</tr>
<tr height="17">
<td align="right"><div align="center">6</div></td>
<td>Fast algorithms for learning deep neural networks <br />
<em>Miguel Carreira-Perpinan and Weiran Wang</em></td>
</tr>
<tr height="17">
<td align="right"><div align="center">2</div></td>
<td>Multi-class classification of independent components of EEG <br />
<em>Laura Frølich, Tobias Andersen and Morten Mørup</em></td>
</tr>
<tr height="17">
<td align="right"><div align="center">5</div></td>
<td>Simple Bandits Revisited <br />
<em>Dorota Glowacka and John Shawe-Taylor</em></td>
</tr>
<tr height="17">
<td align="right"><div align="center">4</div></td>
<td>Multilabel Classification via Random Graph Labeling <br />
<em>Hongyu Su and Juho Rousu</em></td>
</tr>
<tr height="17">
<td align="right"><div align="center">3</div></td>
<td>Machine Learning Markets and alpha-Mixtures <br />
<em>Amos Storkey, Jono Millin and Krzysztof Geras</em></td>
</tr>
</table>
<hr />
<h2><a name="Poster2" id="Poster2"></a>Poster Session II (Sunday 22 April) </h2>
<p>Subjects roughtly include kernels, sparse models, optimization, MCMC, SVMs and Learning theory, networks, classification, and approximate inference. </p> <p>Posters with nearby numbers should be located to each other in the conference hall. Note that the poster board is two meters high one meter wide.</p>
<h3> Contributed Posters </h3>
<table border="1">
<tr>
<th width="8%" scope="col"><div align="center">Poster number </div></th>
<th scope="col" align="center">Paper title<br />
<em>Authors</em></th>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">27</div></td>
<td>Sparse Higher-Order Principal Components Analysis <br />
<em>Genevera Allen </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">49</div></td>
<td>Graphlet decomposition of a weighted network <br />
<em>Hossein Azari Soufiani and Edoardo M. Airoldi</em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">29</div></td>
<td>A General Framework for Structured Sparsity via Proximal Optimization <br />
<em>luca Baldassarre, Jean Morales, Andreas Argyriou and Massimiliano Pontil </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">41</div></td>
<td>Adaptive Metropolis with Online Relabeling <br />
<em>Rémi Bardenet, Olivier Cappé, Gersende Fort and Balázs Kégl </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">20</div></td>
<td>Sample Complexity of Composite Likelihood <br />
<em>Joseph Bradley and Carlos Guestrin </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">42</div></td>
<td>A Family of MCMC Methods on Implicitly Defined Manifolds <br />
<em>Marcus Brubaker, Mathieu Salzmann and Raquel Urtasun </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">19</div></td>
<td>Minimax hypothesis testing for curve registration <br />
<em>Olivier Collier </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">11</div></td>
<td>Fast, Exact Model Selection and Permutation Testing for l2-Regularized Logistic Regression <br />
<em>Bryan Conroy and Paul Sajda </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">46</div></td>
<td>There's a Hole in My Data Space: Piecewise Predictors for Heterogeneous Learning Problems <br />
<em>Ofer Dekel and Ohad Shamir </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">8</div></td>
<td>A metric learning perspective of SVM: on the relation of LMNN and SVM <br />
<em>Huyen Do, Alexandros Kalousis, Jun WANG and Adam Woznica</em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">24</div></td>
<td>Generic Methods for Optimization-Based Modeling <br />
<em>Justin Domke</em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">23</div></td>
<td>Lifted coordinate descent for learning with trace-norm regularization <br />
<em>Miroslav Dudik, Zaid Harchaoui and Jerome Malick</em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">15</div></td>
<td>Error bounds for Kernel Fisher Linear Discriminant in Gaussian Hilbert space <br />
<em>Robert Durrant and Ata Kaban </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">13</div></td>
<td>Copula Network Classifiers (CNCs) <br />
<em>Gal Elidan </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">10</div></td>
<td>A Simple Geometric Interpretation of SVM using Stochastic Adversaries <br />
<em>Roi Livni, Koby Crammer and Amir Globerson </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">47</div></td>
<td>SpeedBoost: Anytime Prediction with Uniform Near-Optimality <br />
<em>Alex Grubb and Drew Bagnell </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">50</div></td>
<td>Subset Infinite Relational Models <br />
<em>Katsuhiko Ishiguro, Naonori Ueda and Hiroshi Sawada </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">6</div></td>
<td>Random Feature Maps for Dot Product Kernels <br />
<em>Purushottam Kar and Harish Karnick </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">43</div></td>
<td>Bayesian Classifier Combination <br />
<em>Hyun-Chul Kim and Zoubin Ghahramani </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">37</div></td>
<td>Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation <br />
<em>J. Zico Kolter and Tommi Jaakkola</em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">22</div></td>
<td>Regularization Paths with Guarantees for Convex Semidefinite Optimization <br />
<em>Joachim Giesen, Martin Jaggi and Soeren Laue </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">14</div></td>
<td>Efficient Distributed Linear Classification Algorithms via the Alternating Direction Method of Multipliers <br />
<em>Caoxie Zhang, Honglak Lee and Kang Shin </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">40</div></td>
<td>Efficient Sampling from Combinatorial Space via Bridging <br />
<em>Dahua Lin and John Fisher </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">38</div></td>
<td>Closed-Form Entropy Limits - A Tool to Monitor Likelihood Optimization of Probabilistic Generative Models <br />
<em>Jörg Lücke and Marc Henniges</em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">21</div></td>
<td>Lifted Linear Programming <br />
<em>Martin Mladenov, Babak Ahmadi and Kristian Kersting </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">39</div></td>
<td>The adversarial stochastic shortest path problem with unknown transition probabilities <br />
<em>Gergely Neu, Andras Gyorgy and Csaba Szepesvari </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">18</div></td>
<td>Beyond Logarithmic Bounds in Online Learning <br />
<em>Francesco Orabona, Nicolò Cesa-Bianchi and Claudio Gentile </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">15</div></td>
<td>Max-Margin Min-Entropy Models <br />
<em>Kevin Miller, M. Pawan Kumar, Ben Packer, Danny Goodman and Daphne Koller</em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">35</div></td>
<td>Approximate Inference by Intersecting Semidefinite Bound and Local Polytope <br />
<em>Jian Peng, Tamir Hazan, Nathan Srebro and Jinbo Xu </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">25</div></td>
<td>Fast interior-point inference in high-dimensional sparse, penalized state-space models <br />
<em>Eftychios Pnevmatikakis and Liam Paninski </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">17</div></td>
<td>Universal Measurement Bounds for Structured Sparse Signal Recovery <br />
<em>Nikhil Rao, Ben Recht and Robert Nowak </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">45</div></td>
<td>Protocols for Learning Classifiers on Distributed Data <br />
<em>Hal Daume III, Jeff Phillips, Avishek Saha and Suresh Venkatasubramanian </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">34</div></td>
<td>Fast Variational Bayesian Inference for Non-Conjugate Matrix Factorization Models <br />
<em>Matthias Seeger and Guillaume Bouchard </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">31</div></td>
<td>Sparsistency of the Edge Lasso over Graphs <br />
<em>James Sharpnack, Aarti Singh and Alessandro Rinaldo </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">15</div></td>
<td>On Bisubmodular Maximization <br />
<em>Ajit Singh, Andrew Guillory and Jeff Bilmes </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">48</div></td>
<td>Testing for Membership to the IFRA and the NBU Classes of Distributions <br />
<em>Radhendushka Srivastava, Ping Li and Debasis Sengupta </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">36</div></td>
<td>Fast Variational Mode-Seeking <br />
<em>Bo Thiesson and Jingu Kim </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">33</div></td>
<td>Primal-Dual methods for sparse constrained matrix completion <br />
<em>Yu Xin and Tommi Jaakkola </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">26</div></td>
<td>Statistical Optimization in High Dimensions <br />
<em>Huan Xu, Constantine Caramanis and Shie Mannor </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">9</div></td>
<td>Perturbation based Large Margin Approach for Ranking <br />
<em>Eunho Yang, Ambuj Tewari and Pradeep Ravikumar </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">11</div></td>
<td>Transductive Learning of Structural SVMs via Prior Knowledge Constraints <br />
<em>Chun-Nam Yu </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">30</div></td>
<td>Locality Preserving Feature Learning <br />
<em>Quanquan Gu, Marina Danilevsky, Zhenhui Li and Jiawei Han </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">7</div></td>
<td>Scaling up Kernel SVM on Limited Resources: A Low-rank Linearization Approach <br />
<em>Kai Zhang, Liang Lan, Zhuang Wang and Fabian Moerchen </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">32</div></td>
<td>Sparse Additive Machine <br />
<em>Tuo Zhao and Han Liu</em></td>
</tr>
<tr height="17">
<td width="8%" height="17" align="right"><div align="center">44</div></td>
<td>Probabilistic acoustic tube: a probabilistic generative model of speech for speech analysis/synthesis <br />
<em>Zhijian Ou and Yang Zhang</em></td>
</tr>
</table>
<h3>
<!-- Breaking news abstracts-->
Posters from Breaking-News Abstracts </h3>
<table border="1">
<tr>
<th width="6%" scope="col"><div align="center">Poster number </div></th>
<th width="614" scope="col" align="center">Paper title<br />
<em>Authors</em></th>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">3</div></td>
<td>Orthogonal foliations: Constrained Riemannian manifold Monte Carlo for hierarchical models <br />
<em>Simon Byrne and Mark Girolami</em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">1</div></td>
<td>Data fusion by kernel combination for behavioural data <br />
<em>Dimitris Fekas</em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">4</div></td>
<td>Detection of recombination events in bacterial genomes from large data sets <br />
<em>Pekka Marttinen</em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">5</div></td>
<td>Data Normalization in the Learning of RBMs <br />
<em>Yichuan Tang and Ilya Sutskever</em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">2</div></td>
<td>Adapting AIC to conditional model selection <br />
<em>Thijs Van Ommen</em></td>
</tr>
</table>
<hr />
<h2><a name="Poster3" id="Poster3"></a>Poster Session III (Monday 23 April) </h2>
<p>Subjects roughly include Topic Models, Nonparametrics, graphical models, random fields, causality, manifold modelling, and computer vision.</p> <p> Posters with nearby numbers should be located to each other in the conference hall. Note that the poster board is two meters high one meter wide.</p>
<h3> Contributed Posters </h3>
<table border="1">
<tr>
<th width="8%" scope="col"><div align="center">Poster number </div></th>
<th scope="col" align="center">Paper title<br />
<em>Authors</em></th>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">34</div></td>
<td>Discriminative Mixtures of Sparse Latent Fields for Risk Management <br />
<em>Felix Agakov, Peter Orchard and Amos Storkey </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">50</div></td>
<td>Factorized Diffusion Map Approximation <br />
<em>Saeed Amizadeh, Hamed Valizadegan and Milos Hauskrecht </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">18</div></td>
<td>Memory-efficient inference in dynamic graphical models using multiple cores <br />
<em>Galen Andrew and Jeff Bilmes </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">44</div></td>
<td>Controlling Selection Bias in Causal Inference <br />
<em>Elias Bareinboim and Judea Pearl </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">14</div></td>
<td>On Sparse, Spectral and Other Parameterizations of Binary Probabilistic Models <br />
<em>David Buchman, Mark Schmidt, Shakir Mohamed, David Poole and Nando de Freitas </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">49</div></td>
<td>Nonlinear low-dimensional regression using auxiliary coordinates <br />
<em>Weiran Wang and Miguel Carreira-Perpinan </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">12</div></td>
<td>Gaussian Processes for time-marked time-series data <br />
<em>John Cunningham, Zoubin Ghahramani and Carl Rasmussen </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">39</div></td>
<td>Wilks' phenomenon and penalized likelihood-ratio test for nonparametric curve registration <br />
<em>Arnak Dalalyan and Olivier Collier </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">46</div></td>
<td>A Nonparametric Bayesian Model for Multiple Clustering with Overlapping Feature Views <br />
<em>Donglin Niu, Jennifer Dy and Zoubin Ghahramani </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">42</div></td>
<td>Statistical test for consistent estimation of causal effects in linear non-Gaussian models <br />
<em>Doris Entner, Patrik Hoyer and Peter Spirtes </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">27</div></td>
<td>Semiparametric Pseudo-Likelihood Estimation in Markov Random Fields <br />
<em>Antonino Freno </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">28</div></td>
<td>Bayesian regularization of non-homogeneous dynamic Bayesian networks by globally coupling <br />
interaction parameters <br />
<em>Marco Grzegorzyk and Dirk Husmeier </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">38</div></td>
<td>Forward Basis Selection for Sparse Approximation over Dictionary <br />
<em>Xiaotong Yuan and Shuicheng Yan </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">40</div></td>
<td>Exchangeability Characterizes Optimality of Sequential Normalized Maximum Likelihood and Bayesian Prediction with Jeffreys Prior <br />
<em>Fares Hedayati and Peter Bartlett </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">7</div></td>
<td>Kernel Topic Models <br />
<em>Philipp Hennig, David Stern, Ralf Herbrich and Thore Graepel </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">26</div></td>
<td>Variable Selection for Gaussian Graphical Models <br />
<em>Jean Honorio, Dimitris Samaras, Irina Rish and Guillermo Cecchi </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">24</div></td>
<td>A Variance Minimization Criterion to Active Learning on Graphs <br />
<em>Ming Ji and Jiawei Han </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">20</div></td>
<td>Detecting Network Cliques with Radon Basis Pursuit <br />
<em>Xiaoye Jiang, Yuan Yao, Han Liu and Leonidas Guibas </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">31</div></td>
<td>A Stick-Breaking Likelihood for Categorical Data Analysis with Latent Gaussian Models <br />
<em>Mohammad Khan, Shakir Mohamed, Benjamin Marlin and Kevin Murphy </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">32</div></td>
<td>High-Dimensional Structured Feature Screening Using Binary Markov Random Fields <br />
<em>Jie Liu, Chunming Zhang, Catherine McCarty, Peggy Peissig, Elizabeth Burnside and David Page </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">17</div></td>
<td>Movement Segmentation and Recognition for Imitation Learning <br />
<em>Franziska Meier, Evangelos Theodorou and Stefan Schaal </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">21</div></td>
<td>Globally Optimizing Graph Partitioning Problems Using Message Passing <br />
<em>Elad Mezuman and Yair Weiss </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">13</div></td>
<td>Bayesian Quadrature for Ratios <br />
<em>Michael Osborne, Roman Garnett, Stephen Roberts, Christopher Hart, Suzanne Aigrain and Neale Gibson </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">45</div></td>
<td>Stick-Breaking Beta Processes and the Poisson Process <br />
<em>John Paisley, David Blei and Michael Jordan </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">48</div></td>
<td>On a Connection between Maximum Variance Unfolding, Shortest Path Problems and IsoMap <br />
<em>Alexander Paprotny and Jochen Garcke </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">30</div></td>
<td>Informative Priors for Markov Blanket Discovery <br />
<em>Adam Pocock, Mikel Lujan and Gavin Brown </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">41</div></td>
<td>Nonparametric Estimation of Conditional Information and Divergences <br />
<em>Barnabas Poczos and Jeff Schneider </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">37</div></td>
<td>Local Anomaly Detection <br />
<em>Venkatesh Saligrama and Manqi Zhao </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">10</div></td>
<td>Markov Logic Mixtures of Gaussian Processes: Towards Machines Reading Regression Data <br />
<em>Martin Schiegg, Marion Neumann and Kristian Kersting </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">23</div></td>
<td>Complexity of Bethe Approximation <br />
<em>Jinwoo Shin </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">16</div></td>
<td>Low rank continuous-space graphical models <br />
<em>Carl Smith, Frank Wood and Liam Paninski </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">47</div></td>
<td>On Nonparametric Guidance for Learning Autoencoder Representations <br />
<em>Jasper Snoek, Ryan Adams and Hugo Larochelle</em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">9</div></td>
<td>Bayesian Inference for Change Points in Dynamical Systems with Reusable States - a Chinese <br />
Restaurant Process Approach <br />
<em>Florian Stimberg, Andreas Ruttor and Manfred Opper </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">25</div></td>
<td>Efficient and Exact MAP-MRF Inference using Branch and Bound <br />
<em>Min Sun, murali telaprolu, Honglak Lee and silvio Savarese </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">33</div></td>
<td>Age-Layered Expectation Maximization for Parameter Learning in Bayesian Networks <br />
<em>Avneesh Saluja, Priya Krishnan Sundararajan and Ole J Mengshoel </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">6</div></td>
<td>On Estimation and Selection for Topic Models <br />
<em>Matt Taddy </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">19</div></td>
<td>Lifted Variable Elimination with Arbitrary Constraints <br />
<em>Nima Taghipour, daan Fierens, Jesse Davis and Hendrik Blockeel </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">15</div></td>
<td>Randomized Optimum Models for Structured Prediction <br />
<em>Daniel Tarlow, Ryan Adams and Richard Zemel </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">8</div></td>
<td>A Hybrid Neural Network-Latent Topic Model <br />
<em>Li Wan, Leo Zhu and Rob Fergus </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">43</div></td>
<td>Causality with Gates <br />
<em>John Winn </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">36</div></td>
<td>Exact Subspace Segmentation and Outlier Detection by Low-Rank Representation <br />
<em>Guangcan Liu, Huan Xu and Shuicheng Yan </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">35</div></td>
<td>Quilting Stochastic Kronecker Product Graphs to Generate Multiplicative Attribute Graphs <br />
<em>Hyokun Yun and S V N Vishwanathan </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">29</div></td>
<td>An Autoregressive Approach to Nonparametric Hierarchical Dependent Modeling <br />
<em>Zhihua Zhang, Dakan Wang and Edward Chang </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">11</div></td>
<td>Learning from Weak Teachers <br />
<em>Ruth Urner, Shai Ben David and Ohad Shamir </em></td>
</tr>
<tr height="17">
<td width="8%" height="17" align="right"><div align="center">22</div></td>
<td>Domain Adaptation: A Small Sample Statistical Approach <br />
<em>Ruslan Salakhutdinov, Sham Kakade and Dean Foster </em></td>
</tr>
<tr height="17">
<td width="8%" height="17" align="right"><div align="center">51</div></td>
<td>Generalized Optimal Reverse Prediction <br />
<em>Martha White and Dale Schuurmans </em></td>
</tr>
</table>
<h3>
<!-- Breaking news abstracts-->
Posters from Breaking-News Abstracts </h3>
<table border="1">
<tr>
<th width="6%" scope="col"><div align="center">Poster number </div></th>
<th width="614" scope="col" align="center">Paper title<br />
<em>Authors</em></th>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">2</div></td>
<td>Inducing Discriminability in Probabilistic Generative Models of Visual Scene Recognition through Fisher Kernels <br />
<em>Tayyaba Azim and Mahesan Niranjan, </em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">5</div></td>
<td>Marginalized Stacked Denoising Auto-encoder <br />
<em>Zhixiang Xu, Minmin Chen, Kilian Weinberger and Fei Sha</em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">4</div></td>
<td>Covariance Selection From Data With Missing Values <br />
<em>Mladen Kolar and Eric Xing</em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">3</div></td>
<td>Spectral Learning of Sparsely Connected Markov Random Fields with Noisy Observations <br />
<em>Gabi Teodoru, Jeff Beck and Maneesh Sahani</em></td>
</tr>
<tr height="17">
<td height="17" align="right"><div align="center">1</div></td>
<td>Generalized HPD-Regions in Fuzzy Bayesian Inference <br />
<em>Reinhard Viertl</em></td>
</tr>
</table>