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AI & Statistics 2012
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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