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title: Accepted Papers
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<h1>AISTATS*2012 Accepted Papers</h1>

<p>Note: the corresponding author is indicated by an asterisk (*).</p>

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<h2>Accepted oral presentations</h2>
<ul>
<li><b>Using More Data to Speed-up Training Time</b> <i>Shai Shalev-Shwartz; Ohad Shamir*; Eran Tromer </i></li>
<li><b>Structured Sparse Canonical Correlation Analysis</b> <i>Xi Chen*; Liu Han; Jaime Carbonell </i></li>
<li><b>Lightning-speed Structure Learning of Nonlinear Continuous Networks</b> <i>Gal Elidan* </i></li>
<li><b>Hierarchical Latent Dictionaries for Models of Brain Activation</b> <i>Alona Fyshe*; Emily Fox; David Dunson; Tom Mitchell </i></li>
<li><b>CorrLog: Correlated Logistic Models for Joint Prediction of Multiple Labels</b> <i>Wei Bian*; Bo Xie; Dacheng Tao </i></li>
<li><b>Efficient Gaussian Process Inference for Short-Scale Spatio-Temporal Modeling</b> <i>Jaakko Luttinen*; Alexander Ilin </i></li>
<li><b>Factorized Asymptotic Bayesian Inference for Mixture Modeling</b> <i>Ryohei Fujimaki*; Satoshi Morinaga </i></li>
<li><b>Marginal Regression For Multitask Learning</b> <i>Mladen Kolar*; Han Liu </i></li>
<li><b>Maximum Margin Temporal Clustering</b> <i>Minh Hoai*; Fernando De la Torre </i></li>
<li><b>High-dimensional Sparse Inverse Covariance Estimation using Greedy Methods</b> <i>Christopher Johnson*; Ali Jalali; Pradeep Ravikumar </i></li>
<li><b>Online Incremental Feature Learning with Denoising Autoencoders </b> <i>Guanyu Zhou; Kihyuk Sohn; Honglak Lee* </i></li>
<li><b>Fast Learning Rate of Multiple Kernel Learning: Trade-Off between Sparsity and Smoothness</b> <i>Taiji Suzuki*; Masashi Sugiyama </i></li>
<li><b>Data dependent kernels in nearly-linear time</b> <i>Guy Lever*; Tom Diethe; John Shawe-Taylor </i></li>
<li><b>Minimax rates for homology inference</b> <i>Sivaraman Balakrishnan*; Alesandro Rinaldo; Don Sheehy; Aarti Singh; Larry Wasserman </i></li>
<li><b>Regression for sets of polynomial equations</b> <i>Franz Király*; Paul von Büenau; Jan Müller; Duncan Blythe; Frank Meinecke; Klaus-Robert Müller </i></li>
<li><b>Online Clustering with Experts</b> <i>Anna Choromanska; Claire Monteleoni* </i></li>
<li><b>Adaptive MCMC with Bayesian Optimization</b> <i>Nimalan Mahendran; Ziyu Wang*; Firas Hamze; Nando de Freitas </i></li>
<li><b>Classifier Cascade for Minimizing Feature Evaluation Cost</b> <i>Minmin Chen*; Zhixiang Xu; Kilian Weinberger; Olivier Chapelle; Dor Kedem </i></li>
<li><b>Minimax Rates of Estimation for Sparse PCA in High Dimensions</b> <i>Vincent Vu*; Jing Lei </i></li>
<li><b>Evaluation of marginal likelihoods via the density of states</b> <i>Michael Habeck* </i></li>
<li><b>Online-to-Confidence-Set Conversions and Application to Sparse Stochastic Bandits </b> <i>Yasin Abbasi-Yadkori*; David Pal; Csaba Szepesvari </i></li>
<li><b>Learning Fourier Sparse Set Functions</b> <i>Peter Stobbe*; Andreas Krause </i></li>
<li><b>A Bayesian Analysis of the Radioactive Releases of Fukushima</b> <i>Ryota Tomioka*; Morten Mørup </i></li>
<li><b>A Differentially Private Stochastic Gradient Descent Algorithm for Multiparty Classification</b> <i>Arun Rajkumar; Shivani Agarwal* </i></li>
</ul>
<h2>Accepted poster presentations</h2>
<ul>
<li><b>Infinite-Dimensional Kalman Filtering Approach to Spatio-Temporal Gaussian Process Regression</b> <i>Simo Särkkä*; Jouni Hartikainen </i></li>
<li><b>On Estimation and Selection for Topic Models </b> <i>Matt Taddy* </i></li>
<li><b>Generalized Optimal Reverse Prediction</b> <i>Martha White*; Dale Schuurmans </i></li>
<li><b>Multi-label Subspace Ensemble</b> <i>Tianyi Zhou*; Dacheng Tao </i></li>
<li><b>On a Connection between Maximum Variance Unfolding, Shortest Path Problems and IsoMap</b> <i>Alexander Paprotny*; Jochen Garcke </i></li>
<li><b>Copula Network Classifiers (CNCs)</b> <i>Gal Elidan* </i></li>
<li><b>Part & Clamp: Efficient Structured Output Learning</b> <i>Patrick Pletscher*; Cheng Soon Ong </i></li>
<li><b>Efficient Sampling from Combinatorial Space via Bridging</b> <i>Dahua Lin*; John Fisher </i></li>
<li><b>Minimax hypothesis testing for curve registration</b> <i>Olivier Collier* </i></li>
<li><b>Kernel Topic Models</b> <i>Philipp Hennig*; David Stern; Ralf Herbrich; Thore Graepel </i></li>
<li><b>Efficient and Exact MAP-MRF Inference using Branch and Bound</b> <i>Min Sun*; murali telaprolu; Honglak Lee; silvio Savarese </i></li>
<li><b>Protocols for Learning Classifiers on Distributed Data </b> <i>Hal Daume III; Jeff Phillips; Avishek Saha*; Suresh Venkatasubramanian </i></li>
<li><b>Variable Selection for Gaussian Graphical Models</b> <i>Jean Honorio*; Dimitris Samaras; Irina Rish; Guillermo Cecchi </i></li>
<li><b>Statistical Optimization in High Dimensions</b> <i>Huan Xu*; Constantine Caramanis; Shie Mannor </i></li>
<li><b>Robust Multi-task Regression with Grossly Corrupted Observations</b> <i>Huan Xu*; Chenlei Leng </i></li>
<li><b>Deep Learning Made Easier by Linear Transformations in Perceptrons</b> <i>Tapani Raiko*; Harri Valpola; Yann LeCun </i></li>
<li><b>Generic Methods for Optimization-Based Modeling</b> <i>Justin Domke* </i></li>
<li><b>Forward Basis Selection for Sparse Approximation over Dictionary</b> <i>Xiaotong Yuan*; Shuicheng Yan </i></li>
<li><b>Closed-Form Entropy Limits -  A Tool to Monitor Likelihood Optimization of Probabilistic Generative Models</b> <i>Jörg Lücke*; Marc Henniges </i></li>
<li><b>Consistency and Rates for Clustering with DBSCAN</b> <i>Bharath Sriperumbudur; Ingo Steinwart* </i></li>
<li><b>A Variance Minimization Criterion to Active Learning on Graphs</b> <i>Ming Ji*; Jiawei Han </i></li>
<li><b>Domain Adaptation: A Small Sample Statistical Approach</b> <i>Ruslan Salakhutdinov*; Sham Kakade; Dean Foster </i></li>
<li><b>Stochastic Bandit Based on Empirical Moments</b> <i>Junya Honda*; Akimichi Takemura </i></li>
<li><b>Regularization Paths with Guarantees for Convex Semidefinite Optimization</b> <i>Joachim Giesen; Martin Jaggi; Soeren Laue* </i></li>
<li><b>Random Feature Maps for Dot Product Kernels</b> <i>Purushottam Kar*; Harish Karnick </i></li>
<li><b>There's a Hole in My Data Space: Piecewise Predictors for Heterogeneous Learning Problems</b> <i>Ofer Dekel*; Ohad Shamir </i></li>
<li><b>Sparse Additive Machine</b> <i>Tuo Zhao*; Han Liu </i></li>
<li><b>Locality Preserving Feature Learning</b> <i>Quanquan Gu*; Marina Danilevsky; Zhenhui Li; Jiawei Han </i></li>
<li><b>Nonlinear low-dimensional regression using auxiliary coordinates</b> <i>Weiran Wang; Miguel Carreira-Perpinan* </i></li>
<li><b>Scalable Inference on Kingman's Coalescent using Pair Similarity </b> <i>Dilan Gorur*; Levi Boyles; Max Welling </i></li>
<li><b>Deep Boltzmann Machines as Feed-Forward Hierarchies</b> <i>Grégoire Montavon*; Mikio Braun; Klaus-Robert Müller </i></li>
<li><b>Gaussian Processes for time-marked time-series data</b> <i>John Cunningham*; Zoubin Ghahramani; Carl Rasmussen </i></li>
<li><b>Wilks' phenomenon and penalized likelihood-ratio test for nonparametric curve registration</b> <i>Arnak Dalalyan*; Olivier Collier </i></li>
<li><b>A metric learning perspective of SVM: on the relation of LMNN and SVM</b> <i>Huyen Do*; Alexandros Kalousis; Jun WANG; Adam Woznica </i></li>
<li><b>Markov Logic Mixtures of Gaussian Processes: Towards Machines Reading Regression Data</b> <i>Martin Schiegg*; Marion Neumann; Kristian Kersting  </i></li>
<li><b>Optimistic planning for Markov decision processes</b> <i>Lucian Busoniu*; Remi Munos </i></li>
<li><b>Bayesian Quadrature for Ratios</b> <i>Michael Osborne*; Roman  Garnett; Stephen Roberts; Christopher Hart; Suzanne Aigrain; Neale Gibson </i></li>
<li><b>Bandit Theory meets Compressed Sensing for high dimensional Stochastic Linear Bandit</b> <i>Alexandra Carpentier*; Remi Munos </i></li>
<li><b>Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing</b> <i>Antoine Bordes*; Xavier Glorot; Jason Weston; Yoshua Bengio </i></li>
<li><b>Adaptive Metropolis with Online Relabeling</b> <i>Rémi Bardenet*; Olivier Cappé; Gersende Fort; Balázs Kégl </i></li>
<li><b>Fast Variational Mode-Seeking</b> <i>Bo Thiesson*; Jingu Kim </i></li>
<li><b>Max-Margin Min-Entropy Models</b> <i>Kevin Miller; M. Pawan Kumar; Ben Packer*; Danny Goodman; Daphne Koller </i></li>
<li><b>Exchangeability Characterizes Optimality of Sequential Normalized Maximum Likelihood and Bayesian Prediction with Jeffreys Prior</b> <i>Fares Hedayati*; Peter Bartlett </i></li>
<li><b>Lifted Variable Elimination with Arbitrary Constraints</b> <i>Nima Taghipour*; daan Fierens; Jesse Davis; Hendrik Blockeel </i></li>
<li><b>Complexity of Bethe Approximation</b> <i>Jinwoo Shin* </i></li>
<li><b>Message-Passing Algorithms for MAP Estimation Using  DC Programming</b> <i>Akshat Kumar*; Shlomo Zilberstein; Marc Toussaint </i></li>
<li><b>Detecting Network Cliques with Radon Basis Pursuit</b> <i>Xiaoye Jiang*; Yuan Yao; Han Liu; Leonidas Guibas </i></li>
<li><b>Multiple Texture Boltzmann Machines</b> <i>Jyri Kivinen*; Christopher Williams </i></li>
<li><b>Nonparametric Estimation of Conditional Information and Divergences</b> <i>Barnabas Poczos*; Jeff Schneider </i></li>
<li><b>Deterministic Annealing for Semi-Supervised Structured Output Learning</b> <i>Paramveer Dhillon*; Sathiya Keerthi; Kedar Bellare; Olivier Chapelle; Sundararajan Sellamanickam </i></li>
<li><b>Fast interior-point inference in high-dimensional sparse, penalized state-space models</b> <i>Eftychios Pnevmatikakis*; Liam Paninski </i></li>
<li><b>Exact Subspace Segmentation and Outlier Detection by Low-Rank Representation</b> <i>Guangcan Liu; Huan Xu*; Shuicheng Yan </i></li>
<li><b>Graphlet decomposition of a weighted network</b> <i>Hossein Azari Soufiani; Edoardo M. Airoldi* </i></li>
<li><b>Efficient Distributed Linear Classification Algorithms via the Alternating Direction Method of Multipliers</b> <i>Caoxie Zhang; Honglak Lee*; Kang Shin </i></li>
<li><b>A Composite Likelihood View for Multi-Label Classification</b> <i>Yi Zhang*; Jeff Schneider </i></li>
<li><b>Subset Infinite Relational Models</b> <i>Katsuhiko Ishiguro*; Naonori Ueda; Hiroshi Sawada </i></li>
<li><b>Bayesian Classifier Combination</b> <i>Hyun-Chul Kim*; Zoubin Ghahramani </i></li>
<li><b>Globally Optimizing Graph Partitioning Problems Using Message Passing</b> <i>Elad Mezuman*; Yair Weiss </i></li>
<li><b>Learning Low-order Models for Enforcing High-order Statistics</b> <i>Patrick Pletscher*; Pushmeet Kohli </i></li>
<li><b>Bayesian Group Factor Analysis</b> <i>Seppo Virtanen; Arto Klami*; Suleiman Khan; Samuel Kaski </i></li>
<li><b>Statistical test for consistent estimation of causal effects in linear non-Gaussian models</b> <i>Doris Entner*; Patrik Hoyer; Peter Spirtes </i></li>
<li><b>History-alignment models for bias-aware prediction of virological response to HIV combination therapy</b> <i>Jasmina Bogojeska*; Daniel Stöckel; Maurizio Zazzi; Rolf Kaiser; Francesca Incardona; Michal Rosen-Zvi; Thomas Lengauer </i></li>
<li><b>Semiparametric Pseudo-Likelihood Estimation in Markov Random Fields</b> <i>Antonino Freno* </i></li>
<li><b>Bayesian regularization of non-homogeneous dynamic Bayesian networks by globally coupling interaction parameters</b> <i>Marco Grzegorzyk*; Dirk Husmeier </i></li>
<li><b>Exploiting Unrelated Tasks in Multi-Task Learning</b> <i>bernardino Romera Paredes; Andreas Argyriou; Nadia Berthouze; Massimiliano Pontil* </i></li>
<li><b>Causality with Gates</b> <i>John Winn* </i></li>
<li><b>No Internal Regret via Neighborhood Watch</b> <i>Dean Foster; Alexander Rakhlin* </i></li>
<li><b>Controlling Selection Bias in Causal Inference </b> <i>Elias Bareinboim*; Judea Pearl </i></li>
<li><b>Multi-armed Bandit Problems with History</b> <i>Pannagadatta Shivaswamy*; Thorsten Joachims </i></li>
<li><b>A Family of MCMC Methods on Implicitly Defined Manifolds</b> <i>Marcus Brubaker*; Mathieu Salzmann; Raquel Urtasun </i></li>
<li><b>The adversarial stochastic shortest path problem with unknown transition probabilities</b> <i>Gergely Neu*; Andras Gyorgy; Csaba Szepesvari </i></li>
<li><b>Fast, Exact Model Selection and Permutation Testing for l2-Regularized Logistic Regression</b> <i>Bryan Conroy*; Paul Sajda </i></li>
<li><b>Approximate Inference by Intersecting Semidefinite Bound and Local Polytope</b> <i>Jian Peng*; Tamir Hazan; Nathan Srebro; Jinbo Xu </i></li>
<li><b>An Autoregressive Approach to Nonparametric Hierarchical Dependent Modeling</b> <i>Zhihua Zhang*; Dakan Wang; Edward Chang </i></li>
<li><b>Perturbation based Large Margin Approach for Ranking</b> <i>Eunho Yang*; Ambuj Tewari; Pradeep Ravikumar </i></li>
<li><b>A Simple Geometric Interpretation of SVM using Stochastic Adversaries</b> <i>Roi Livni; Koby Crammer; Amir Globerson* </i></li>
<li><b>Lifted Linear Programming</b> <i>Martin Mladenov*; Babak Ahmadi; Kristian Kersting  </i></li>
<li><b>Error bounds for Kernel Fisher Linear Discriminant in Gaussian Hilbert space</b> <i>Robert Durrant*; Ata Kaban </i></li>
<li><b>Krylov Subspace Descent for Deep Learning</b> <i>Oriol Vinyals*; Daniel Povey </i></li>
<li><b>Scalable Personalization of Long-Term Physiological Monitoring: Active Learning Methodologies for Epileptic Seizure Onset Detection</b> <i>Guha Balakrishnan; Zeeshan Syed* </i></li>
<li><b>Informative Priors for Markov Blanket Discovery</b> <i>Adam Pocock*; Mikel Lujan; Gavin Brown </i></li>
<li><b>On Nonparametric Guidance for Learning Autoencoder Representations </b> <i>Jasper Snoek*; Ryan Adams; Hugo Larochelle </i></li>
<li><b>On Bayesian Upper Confidence Bounds for Bandit Problems</b> <i>Emilie Kaufmann*; Olivier Cappé; Aurélien Garivier </i></li>
<li><b>Bayesian Comparison of Machine Learning Algorithms on Single and Multiple Datasets</b> <i>Alexandre Lacoste*; Francois Laviolette; Mario Marchand </i></li>
<li><b>Local Anomaly Detection</b> <i>Venkatesh Saligrama*; Manqi Zhao </i></li>
<li><b>A General Framework for Structured Sparsity via Proximal Optimization</b> <i>luca Baldassarre*; Jean Morales; Andreas Argyriou; Massimiliano Pontil </i></li>
<li><b>Universal Measurement Bounds for Structured Sparse Signal Recovery</b> <i>Nikhil Rao*; Ben Recht; Robert Nowak </i></li>
<li><b>A Stick-Breaking Likelihood for Categorical Data Analysis with Latent Gaussian Models</b> <i>Mohammad Khan*; Shakir Mohamed; Benjamin Marlin; Kevin Murphy </i></li>
<li><b>Learning from Weak Teachers</b> <i>Ruth Urner*; Shai Ben David; Ohad Shamir </i></li>
<li><b>Beyond Logarithmic Bounds in Online Learning</b> <i>Francesco Orabona*; Nicolò Cesa-Bianchi; Claudio Gentile </i></li>
<li><b>High-Dimensional Structured Feature Screening Using Binary Markov Random Fields</b> <i>Jie Liu*; Chunming Zhang; Catherine McCarty; Peggy Peissig; Elizabeth Burnside; David Page </i></li>
<li><b>Factorized Diffusion Map Approximation</b> <i>Saeed Amizadeh*; Hamed Valizadegan; Milos Hauskrecht </i></li>
<li><b>Contextual Bandit Learning with Predictable Rewards</b> <i>Alekh Agarwal*; Miroslav Dudik; Satyen Kale; John Langford; Robert Schapire </i></li>
<li><b>A Hybrid Neural Network-Latent Topic Model</b> <i>Li Wan*; Leo Zhu; Rob Fergus </i></li>
<li><b>Stick-Breaking Beta Processes and the Poisson Process</b> <i>John Paisley*; David Blei; Michael Jordan </i></li>
<li><b>On Bisubmodular Maximization</b> <i>Ajit Singh*; Andrew Guillory; Jeff Bilmes </i></li>
<li><b>Bayesian Inference for Change Points in Dynamical Systems with Reusable States - a Chinese Restaurant Process Approach</b> <i>Florian Stimberg*; Andreas Ruttor; Manfred Opper </i></li>
<li><b>A Two-Graph Guided Multi-task Lasso Approach for eQTL Mapping</b> <i>Xiaohui Chen; Xinghua Shi*; Xing Xu; Zhiyong Wang; Ryan Mills; Charles Lee; jinbo Xu </i></li>
<li><b>Information Theoretic Model Validation for Spectral Clustering</b> <i>Morteza Haghir Chehreghani*; Alberto Giovanni Busetto; Joachim M. Buhmann </i></li>
<li><b>Hierarchical Relative Entropy Policy Search</b> <i>Christian Daniel*; Gerhard Neumann; Jan Peters </i></li>
<li><b>Online Clustering of Processes</b> <i>Azadeh Khaleghi*; Daniil Ryabko; Jeremie Mary; Philippe Preux </i></li>
<li><b>Movement Segmentation and Recognition for Imitation Learning</b> <i>Franziska Meier*; Evangelos Theodorou; Stefan Schaal </i></li>
<li><b>Beta-Negative Binomial Process and Poisson Factor Analysis</b> <i>Mingyuan Zhou*; Lauren Hannah; David Dunson; Lawrence Carin </i></li>
<li><b>On Average Reward Policy Evaluation in Infinite-State Partially Observable Systems</b> <i>Yuri Grinberg*; Doina Precup </i></li>
<li><b>On Sparse, Spectral and Other Parameterizations of Binary Probabilistic Models</b> <i>David Buchman*; Mark Schmidt; Shakir Mohamed; David Poole; nando de Freitas </i></li>
<li><b>Scaling up Kernel SVM on Limited Resources: A Low-rank Linearization Approach</b> <i>Kai Zhang*; Liang Lan; Zhuang Wang; Fabian Moerchen </i></li>
<li><b>UPAL: Unbiased Pool Based Active Learning</b> <i>Ravi Ganti*; Alexander Gray </i></li>
<li><b>High-Rank Matrix Completion</b> <i>Brian Eriksson; Laura Balzano; Robert Nowak* </i></li>
<li><b>Lifted coordinate descent for learning with trace-norm regularization</b> <i>Miroslav Dudik*; Zaid Harchaoui; Jerome Malick </i></li>
<li><b>Testing for Membership to the IFRA and the NBU Classes of Distributions</b> <i>Radhendushka Srivastava*; Ping Li; Debasis Sengupta </i></li>
<li><b>A Nonparametric Bayesian Model for Multiple Clustering with Overlapping Feature Views</b> <i>Donglin Niu; Jennifer Dy*; Zoubin Ghahramani </i></li>
<li><b>Primal-Dual methods for sparse constrained matrix completion</b> <i>Yu Xin*; Tommi Jaakkola </i></li>
<li><b>Fast Variational Bayesian Inference for Non-Conjugate Matrix Factorization Models</b> <i>Matthias Seeger*; Guillaume Bouchard </i></li>
<li><b>Efficient Hypergraph Clustering</b> <i>Marius Leordeanu*; Cristian Sminchisescu </i></li>
<li><b>Sparse Higher-Order Principal Components Analysis</b> <i>Genevera Allen* </i></li>
<li><b>Flexible Martingale Priors for Deep Hierarchies</b> <i>Jacob Steinhardt*; Zoubin Ghahramani </i></li>
<li><b>Age-Layered Expectation Maximization for Parameter Learning in Bayesian Networks</b> <i>Avneesh Saluja; Priya Krishnan Sundararajan*; Ole J Mengshoel </i></li>
<li><b>Constrained 1-Spectral Clustering</b> <i>Syama Sundar Rangapuram*; Matthias Hein </i></li>
<li><b>Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation</b> <i>J. Zico Kolter*; Tommi Jaakkola </i></li>
<li><b>Quilting Stochastic Kronecker Product Graphs to Generate Multiplicative Attribute Graphs</b> <i>Hyokun Yun*; S V N Vishwanathan </i></li>
<li><b>Structured Output Learning with High Order Loss Functions</b> <i>Daniel Tarlow*; Richard Zemel </i></li>
<li><b>Joint Estimation of Structured Sparsity and Output Structure in Multiple-Output Regression via Inverse-Covariance Regularization</b> <i>Kyung-Ah Sohn; Seyoung  Kim* </i></li>
<li><b>Low rank continuous-space graphical models</b> <i>Carl Smith*; Frank Wood; Liam Paninski </i></li>
<li><b>Transductive Learning of Structural SVMs via Prior Knowledge Constraints</b> <i>Chun-Nam Yu* </i></li>
<li><b>Sample Complexity of Composite Likelihood</b> <i>Joseph Bradley*; Carlos Guestrin </i></li>
<li><b>Sparsistency of the Edge Lasso over Graphs</b> <i>James Sharpnack*; Aarti Singh; Alessandro Rinaldo </i></li>
<li><b>SpeedBoost: Anytime Prediction with Uniform Near-Optimality</b> <i>Alex Grubb*; Drew Bagnell </i></li>
<li><b>Randomized Optimum Models for Structured Prediction</b> <i>Daniel Tarlow*; Ryan Adams; Richard Zemel </i></li>
<li><b>Active Learning from Multiple Knowledge Sources</b> <i>Yan Yan; Romer Rosales*; Glenn Fung; Faisal Farooq; Bharat Rao; Jennifer Dy </i></li>
<li><b>Multiresolution Deep Belief Networks</b> <i>Yichuan Tang*; Abdel-rahman Mohamed </i></li>
<li><b>Discriminative Mixtures of Sparse Latent Fields for Risk Management</b> <i>Felix Agakov*; Peter Orchard; Amos Storkey </i></li>
<li><b>Probabilistic acoustic tube: a probabilistic generative model of speech for speech analysis/synthesis</b> <i>Zhijian Ou*; Yang Zhang </i></li>
<li><b>Memory-efficient inference in dynamic graphical models using multiple cores</b> <i>Galen Andrew*; Jeff Bilmes </i></li>
</ul>