[edit]

AISTATS*2012 Poster Sessions

There are three poster sessions: Poster Session I, Poster Session II, and Poster Session III. Each poster has a poster number. 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.

Poster Session I (Saturday 21 April)

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.

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.

Contributed Posters

Poster number	Paper title Authors
34	Contextual Bandit Learning with Predictable Rewards Alekh Agarwal, Miroslav Dudik, Satyen Kale, John Langford and Robert Schapire
44	History-alignment models for bias-aware prediction of virological response to HIV combination therapy Jasmina Bogojeska, Daniel Stöckel, Maurizio Zazzi, Rolf Kaiser, Francesca Incardona, Michal Rosen-Zvi and Thomas Lengauer
42	Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing Antoine Bordes, Xavier Glorot, Jason Weston and Yoshua Bengio
36	Optimistic planning for Markov decision processes Lucian Busoniu and Remi Munos
31	Bandit Theory meets Compressed Sensing for high dimensional Stochastic Linear Bandit Alexandra Carpentier and Remi Munos
38	Hierarchical Relative Entropy Policy Search Christian Daniel, Gerhard Neumann and Jan Peters
13	Deterministic Annealing for Semi-Supervised Structured Output Learning Paramveer Dhillon, Sathiya Keerthi, Kedar Bellare, Olivier Chapelle and Sundararajan Sellamanickam
40	UPAL: Unbiased Pool Based Active Learning Ravi Ganti and Alexander Gray
18	Scalable Inference on Kingman's Coalescent using Pair Similarity  Dilan Gorur, Levi Boyles and Max Welling
37	On Average Reward Policy Evaluation in Infinite-State Partially Observable Systems Yuri Grinberg and Doina Precup
19	Information Theoretic Model Validation for Spectral Clustering Morteza Haghir Chehreghani, Alberto Giovanni Busetto and Joachim M. Buhmann
30	Stochastic Bandit Based on Empirical Moments Junya Honda and Akimichi Takemura
33	On Bayesian Upper Confidence Bounds for Bandit Problems Emilie Kaufmann, Olivier Cappé and Aurélien Garivier
23	Online Clustering of Processes Azadeh Khaleghi, Daniil Ryabko, Jeremie Mary and Philippe Preux
11	Joint Estimation of Structured Sparsity and Output Structure in Multiple-Output Regression via Inverse-Covariance Regularization Kyung-Ah Sohn and Seyoung Kim
26	Multiple Texture Boltzmann Machines Jyri Kivinen and Christopher Williams
47	Bayesian Group Factor Analysis Seppo Virtanen, Arto Klami, Suleiman Khan and Samuel Kaski
7	Bayesian Comparison of Machine Learning Algorithms on Single and Multiple Datasets Alexandre Lacoste, Francois Laviolette and Mario Marchand
20	Efficient Hypergraph Clustering Marius Leordeanu and Cristian Sminchisescu
25	Deep Boltzmann Machines as Feed-Forward Hierarchies Grégoire Montavon, Mikio Braun and Klaus-Robert Müller
48	High-Rank Matrix Completion Brian Eriksson, Laura Balzano and Robert Nowak
9	Part & Clamp: Efficient Structured Output Learning Patrick Pletscher and Cheng Soon Ong
12	Learning Low-order Models for Enforcing High-order Statistics Patrick Pletscher and Pushmeet Kohli
14	Exploiting Unrelated Tasks in Multi-Task Learning bernardino Romera Paredes, Andreas Argyriou, Nadia Berthouze and Massimiliano Pontil
24	Deep Learning Made Easier by Linear Transformations in Perceptrons Tapani Raiko, Harri Valpola and Yann LeCun
35	No Internal Regret via Neighborhood Watch Dean Foster and Alexander Rakhlin
21	Constrained 1-Spectral Clustering Syama Sundar Rangapuram and Matthias Hein
17	Active Learning from Multiple Knowledge Sources Yan Yan, Romer Rosales, Glenn Fung, Faisal Farooq, Bharat Rao and Jennifer Dy
43	A Two-Graph Guided Multi-task Lasso Approach for eQTL Mapping Xiaohui Chen, Xinghua Shi, Xing Xu, Zhiyong Wang, Ryan Mills, Charles Lee and jinbo Xu
32	Multi-armed Bandit Problems with History Pannagadatta Shivaswamy and Thorsten Joachims
28	Flexible Martingale Priors for Deep Hierarchies Jacob Steinhardt and Zoubin Ghahramani
22	Consistency and Rates for Clustering with DBSCAN Bharath Sriperumbudur and Ingo Steinwart
45	Scalable Personalization of Long-Term Physiological Monitoring: Active Learning Methodologies for Epileptic Seizure Onset Detection Guha Balakrishnan and Zeeshan Syed
29	Multiresolution Deep Belief Networks Yichuan Tang and Abdel-rahman Mohamed
10	Structured Output Learning with High Order Loss Functions Daniel Tarlow and Richard Zemel
27	Krylov Subspace Descent for Deep Learning Oriol Vinyals and Daniel Povey
8	Robust Multi-task Regression with Grossly Corrupted Observations Huan Xu and Chenlei Leng
16	A Composite Likelihood View for Multi-Label Classification Yi Zhang and Jeff Schneider
49	Beta-Negative Binomial Process and Poisson Factor Analysis Mingyuan Zhou, Lauren Hannah, David Dunson and Lawrence Carin
15	Multi-label Subspace Ensemble Tianyi Zhou and Dacheng Tao
50	Infinite-Dimensional Kalman Filtering Approach to Spatio-Temporal Gaussian Process Regression Simo Särkkä and Jouni Hartikainen
46	Message-Passing Algorithms for MAP Estimation Using  DC Programming Akshat Kumar, Shlomo Zilberstein and Marc Toussaint

Posters from Breaking-News Abstracts

Poster number	Paper title Authors
1	The effect of subsample size in Stability Selection Gilles Blanchard, Andre Beinrucker and Urun Dogan
6	Fast algorithms for learning deep neural networks Miguel Carreira-Perpinan and Weiran Wang
2	Multi-class classification of independent components of EEG Laura Frølich, Tobias Andersen and Morten Mørup
5	Simple Bandits Revisited Dorota Glowacka and John Shawe-Taylor
4	Multilabel Classification via Random Graph Labeling Hongyu Su and Juho Rousu
3	Machine Learning Markets and alpha-Mixtures Amos Storkey, Jono Millin and Krzysztof Geras

---

Poster Session II (Sunday 22 April)

Subjects roughtly include kernels, sparse models, optimization, MCMC, SVMs and Learning theory, networks, classification, and approximate inference.

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.

Contributed Posters

Poster number	Paper title Authors
27	Sparse Higher-Order Principal Components Analysis Genevera Allen
49	Graphlet decomposition of a weighted network Hossein Azari Soufiani and Edoardo M. Airoldi
29	A General Framework for Structured Sparsity via Proximal Optimization luca Baldassarre, Jean Morales, Andreas Argyriou and Massimiliano Pontil
41	Adaptive Metropolis with Online Relabeling Rémi Bardenet, Olivier Cappé, Gersende Fort and Balázs Kégl
20	Sample Complexity of Composite Likelihood Joseph Bradley and Carlos Guestrin
42	A Family of MCMC Methods on Implicitly Defined Manifolds Marcus Brubaker, Mathieu Salzmann and Raquel Urtasun
19	Minimax hypothesis testing for curve registration Olivier Collier
11	Fast, Exact Model Selection and Permutation Testing for l2-Regularized Logistic Regression Bryan Conroy and Paul Sajda
46	There's a Hole in My Data Space: Piecewise Predictors for Heterogeneous Learning Problems Ofer Dekel and Ohad Shamir
8	A metric learning perspective of SVM: on the relation of LMNN and SVM Huyen Do, Alexandros Kalousis, Jun WANG and Adam Woznica
24	Generic Methods for Optimization-Based Modeling Justin Domke
23	Lifted coordinate descent for learning with trace-norm regularization Miroslav Dudik, Zaid Harchaoui and Jerome Malick
15	Error bounds for Kernel Fisher Linear Discriminant in Gaussian Hilbert space Robert Durrant and Ata Kaban
13	Copula Network Classifiers (CNCs) Gal Elidan
10	A Simple Geometric Interpretation of SVM using Stochastic Adversaries Roi Livni, Koby Crammer and Amir Globerson
47	SpeedBoost: Anytime Prediction with Uniform Near-Optimality Alex Grubb and Drew Bagnell
50	Subset Infinite Relational Models Katsuhiko Ishiguro, Naonori Ueda and Hiroshi Sawada
6	Random Feature Maps for Dot Product Kernels Purushottam Kar and Harish Karnick
43	Bayesian Classifier Combination Hyun-Chul Kim and Zoubin Ghahramani
37	Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation J. Zico Kolter and Tommi Jaakkola
22	Regularization Paths with Guarantees for Convex Semidefinite Optimization Joachim Giesen, Martin Jaggi and Soeren Laue
14	Efficient Distributed Linear Classification Algorithms via the Alternating Direction Method of Multipliers Caoxie Zhang, Honglak Lee and Kang Shin
40	Efficient Sampling from Combinatorial Space via Bridging Dahua Lin and John Fisher
38	Closed-Form Entropy Limits -  A Tool to Monitor Likelihood Optimization of Probabilistic Generative Models Jörg Lücke and Marc Henniges
21	Lifted Linear Programming Martin Mladenov, Babak Ahmadi and Kristian Kersting
39	The adversarial stochastic shortest path problem with unknown transition probabilities Gergely Neu, Andras Gyorgy and Csaba Szepesvari
18	Beyond Logarithmic Bounds in Online Learning Francesco Orabona, Nicolò Cesa-Bianchi and Claudio Gentile
15	Max-Margin Min-Entropy Models Kevin Miller, M. Pawan Kumar, Ben Packer, Danny Goodman and Daphne Koller
35	Approximate Inference by Intersecting Semidefinite Bound and Local Polytope Jian Peng, Tamir Hazan, Nathan Srebro and Jinbo Xu
25	Fast interior-point inference in high-dimensional sparse, penalized state-space models Eftychios Pnevmatikakis and Liam Paninski
17	Universal Measurement Bounds for Structured Sparse Signal Recovery Nikhil Rao, Ben Recht and Robert Nowak
45	Protocols for Learning Classifiers on Distributed Data  Hal Daume III, Jeff Phillips, Avishek Saha and Suresh Venkatasubramanian
34	Fast Variational Bayesian Inference for Non-Conjugate Matrix Factorization Models Matthias Seeger and Guillaume Bouchard
31	Sparsistency of the Edge Lasso over Graphs James Sharpnack, Aarti Singh and Alessandro Rinaldo
15	On Bisubmodular Maximization Ajit Singh, Andrew Guillory and Jeff Bilmes
48	Testing for Membership to the IFRA and the NBU Classes of Distributions Radhendushka Srivastava, Ping Li and Debasis Sengupta
36	Fast Variational Mode-Seeking Bo Thiesson and Jingu Kim
33	Primal-Dual methods for sparse constrained matrix completion Yu Xin and Tommi Jaakkola
26	Statistical Optimization in High Dimensions Huan Xu, Constantine Caramanis and Shie Mannor
9	Perturbation based Large Margin Approach for Ranking Eunho Yang, Ambuj Tewari and Pradeep Ravikumar
11	Transductive Learning of Structural SVMs via Prior Knowledge Constraints Chun-Nam Yu
30	Locality Preserving Feature Learning Quanquan Gu, Marina Danilevsky, Zhenhui Li and Jiawei Han
7	Scaling up Kernel SVM on Limited Resources: A Low-rank Linearization Approach Kai Zhang, Liang Lan, Zhuang Wang and Fabian Moerchen
32	Sparse Additive Machine Tuo Zhao and Han Liu
44	Probabilistic acoustic tube: a probabilistic generative model of speech for speech analysis/synthesis Zhijian Ou and Yang Zhang

Posters from Breaking-News Abstracts

Poster number	Paper title Authors
3	Orthogonal foliations: Constrained Riemannian manifold Monte Carlo for hierarchical models Simon Byrne and Mark Girolami
1	Data fusion by kernel combination for behavioural data Dimitris Fekas
4	Detection of recombination events in bacterial genomes from large data sets Pekka Marttinen
5	Data Normalization in the Learning of RBMs Yichuan Tang and Ilya Sutskever
2	Adapting AIC to conditional model selection Thijs Van Ommen

---

Poster Session III (Monday 23 April)

Subjects roughly include Topic Models, Nonparametrics, graphical models, random fields, causality, manifold modelling, and computer vision.

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.

Contributed Posters

Poster number	Paper title Authors
34	Discriminative Mixtures of Sparse Latent Fields for Risk Management Felix Agakov, Peter Orchard and Amos Storkey
50	Factorized Diffusion Map Approximation Saeed Amizadeh, Hamed Valizadegan and Milos Hauskrecht
18	Memory-efficient inference in dynamic graphical models using multiple cores Galen Andrew and Jeff Bilmes
44	Controlling Selection Bias in Causal Inference  Elias Bareinboim and Judea Pearl
14	On Sparse, Spectral and Other Parameterizations of Binary Probabilistic Models David Buchman, Mark Schmidt, Shakir Mohamed, David Poole and Nando de Freitas
49	Nonlinear low-dimensional regression using auxiliary coordinates Weiran Wang and Miguel Carreira-Perpinan
12	Gaussian Processes for time-marked time-series data John Cunningham, Zoubin Ghahramani and Carl Rasmussen
39	Wilks' phenomenon and penalized likelihood-ratio test for nonparametric curve registration Arnak Dalalyan and Olivier Collier
46	A Nonparametric Bayesian Model for Multiple Clustering with Overlapping Feature Views Donglin Niu, Jennifer Dy and Zoubin Ghahramani
42	Statistical test for consistent estimation of causal effects in linear non-Gaussian models Doris Entner, Patrik Hoyer and Peter Spirtes
27	Semiparametric Pseudo-Likelihood Estimation in Markov Random Fields Antonino Freno
28	Bayesian regularization of non-homogeneous dynamic Bayesian networks by globally coupling interaction parameters Marco Grzegorzyk and Dirk Husmeier
38	Forward Basis Selection for Sparse Approximation over Dictionary Xiaotong Yuan and Shuicheng Yan
40	Exchangeability Characterizes Optimality of Sequential Normalized Maximum Likelihood and Bayesian Prediction with Jeffreys Prior Fares Hedayati and Peter Bartlett
7	Kernel Topic Models Philipp Hennig, David Stern, Ralf Herbrich and Thore Graepel
26	Variable Selection for Gaussian Graphical Models Jean Honorio, Dimitris Samaras, Irina Rish and Guillermo Cecchi
24	A Variance Minimization Criterion to Active Learning on Graphs Ming Ji and Jiawei Han
20	Detecting Network Cliques with Radon Basis Pursuit Xiaoye Jiang, Yuan Yao, Han Liu and Leonidas Guibas
31	A Stick-Breaking Likelihood for Categorical Data Analysis with Latent Gaussian Models Mohammad Khan, Shakir Mohamed, Benjamin Marlin and Kevin Murphy
32	High-Dimensional Structured Feature Screening Using Binary Markov Random Fields Jie Liu, Chunming Zhang, Catherine McCarty, Peggy Peissig, Elizabeth Burnside and David Page
17	Movement Segmentation and Recognition for Imitation Learning Franziska Meier, Evangelos Theodorou and Stefan Schaal
21	Globally Optimizing Graph Partitioning Problems Using Message Passing Elad Mezuman and Yair Weiss
13	Bayesian Quadrature for Ratios Michael Osborne, Roman Garnett, Stephen Roberts, Christopher Hart, Suzanne Aigrain and Neale Gibson
45	Stick-Breaking Beta Processes and the Poisson Process John Paisley, David Blei and Michael Jordan
48	On a Connection between Maximum Variance Unfolding, Shortest Path Problems and IsoMap Alexander Paprotny and Jochen Garcke
30	Informative Priors for Markov Blanket Discovery Adam Pocock, Mikel Lujan and Gavin Brown
41	Nonparametric Estimation of Conditional Information and Divergences Barnabas Poczos and Jeff Schneider
37	Local Anomaly Detection Venkatesh Saligrama and Manqi Zhao
10	Markov Logic Mixtures of Gaussian Processes: Towards Machines Reading Regression Data Martin Schiegg, Marion Neumann and Kristian Kersting
23	Complexity of Bethe Approximation Jinwoo Shin
16	Low rank continuous-space graphical models Carl Smith, Frank Wood and Liam Paninski
47	On Nonparametric Guidance for Learning Autoencoder Representations  Jasper Snoek, Ryan Adams and Hugo Larochelle
9	Bayesian Inference for Change Points in Dynamical Systems with Reusable States - a Chinese Restaurant Process Approach Florian Stimberg, Andreas Ruttor and Manfred Opper
25	Efficient and Exact MAP-MRF Inference using Branch and Bound Min Sun, murali telaprolu, Honglak Lee and silvio Savarese
33	Age-Layered Expectation Maximization for Parameter Learning in Bayesian Networks Avneesh Saluja, Priya Krishnan Sundararajan and Ole J Mengshoel
6	On Estimation and Selection for Topic Models  Matt Taddy
19	Lifted Variable Elimination with Arbitrary Constraints Nima Taghipour, daan Fierens, Jesse Davis and Hendrik Blockeel
15	Randomized Optimum Models for Structured Prediction Daniel Tarlow, Ryan Adams and Richard Zemel
8	A Hybrid Neural Network-Latent Topic Model Li Wan, Leo Zhu and Rob Fergus
43	Causality with Gates John Winn
36	Exact Subspace Segmentation and Outlier Detection by Low-Rank Representation Guangcan Liu, Huan Xu and Shuicheng Yan
35	Quilting Stochastic Kronecker Product Graphs to Generate Multiplicative Attribute Graphs Hyokun Yun and S V N Vishwanathan
29	An Autoregressive Approach to Nonparametric Hierarchical Dependent Modeling Zhihua Zhang, Dakan Wang and Edward Chang
11	Learning from Weak Teachers Ruth Urner, Shai Ben David and Ohad Shamir
22	Domain Adaptation: A Small Sample Statistical Approach Ruslan Salakhutdinov, Sham Kakade and Dean Foster
51	Generalized Optimal Reverse Prediction Martha White and Dale Schuurmans

Posters from Breaking-News Abstracts

Poster number	Paper title Authors
2	Inducing Discriminability in Probabilistic Generative Models of Visual Scene Recognition through Fisher Kernels Tayyaba Azim and Mahesan Niranjan,
5	Marginalized Stacked Denoising Auto-encoder Zhixiang Xu, Minmin Chen, Kilian Weinberger and Fei Sha
4	Covariance Selection From Data With Missing Values Mladen Kolar and Eric Xing
3	Spectral Learning of Sparsely Connected Markov Random Fields with Noisy Observations Gabi Teodoru, Jeff Beck and Maneesh Sahani
1	Generalized HPD-Regions in Fuzzy Bayesian Inference Reinhard Viertl