| Poster Session I - Thursday 5:15pm-8:00pm |
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| | Hash Kernels; Q. Shi, J. Petterson, G. Dror, J. Langford, A. Smola, A. Strehl, V. Vishwanathan |
| | On Partitioning Rules for Bipartite Ranking; S. Clemencon, N. Vayatis |
| | Variational Learning of Inducing Variables in Sparse Gaussian Processes; M. Titsias |
| | Group Nonnegative Matrix Factorization for EEG Classification; H. Lee, S. Choi |
| | Markov Topic Models; C. Wang, B. Thiesson, C. Meek, D. Blei |
| | Large-Margin Structured Prediction via Linear Programming; Z. Wang, J. Shawe-Taylor |
| | MCMC Methods for Bayesian Mixtures of Copulas; R. Silva, R. Gramacy |
| | A New Perspective for Information Theoretic Feature Selection; G. Brown |
| | Variational Bridge Regression; A. Armagan |
| | Spanning Tree Approximations for Conditional Random Fields; P. Pletscher, C. Ong, J. Buhmann |
| | Sampling Techniques for the Nystrom Method; S. Kumar, M. Mohri, A. Talwalkar |
| | Statistical and Computational Tradeoffs in Stochastic Composite Likelihood; J. Dillon, G. Lebanon |
| | Variable Metric Stochastic Approximation Theory; P. Sunehag, J. Trumpf, S. Vishwanathan, N. Schraudolph |
| | Coherence Functions for Multicategory Margin-based Classification Methods; Z. zhang, M. Jordan, W. Li, D. Yeung |
| | Lanczos Approximations for the Speedup of Kernel Partial Least Squares Regression; N. Kramer, M. Sugiyama, M. Braun. |
| | Efficient Graphlet Kernels for Large Graph Comparison; N. Shervashidze, S. Vishwanathan, T. Petri, K. Mehlhorn, K. Borgwardt |
| | Reversible Jump MCMC for Non-Negative Matrix Factorisation; M. Zhong, M. Girolami |
| | Convex Perturbations for Scalable Semidefinite Programming; B. Kulis, S. Sra, I. Dhillon |
| | Tree Block Coordinate Descent for MAP in Graphical Models; D. Sontag, T. Jaakkola |
| | Tractable Search for Learning Exponential Models of Rankings; B. Mandhani, M. Meila |
| Poster Session II - Friday 7:30pm-10:30pm |
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| | Maximum Entropy Density Estimation with Incomplete Presence-Only Data; B. Huang, A. Salleb-Aouissi |
| | Structure Identification by Optimized Interventions; A. Busetto, J. Buhmann |
| | Learning the Switching Rate by Discretising Bernoulli Sources Online; S. de Rooij, T. van Erven |
| | An Information Geometry Approach for Distance Metric Learning; S. Wang, R. Jin |
| | PAC-Bayesian Generalization Bound for Density Estimation with Application to Co-clustering; Y. Seldin, N. Tishby |
| | Estimating Tree-Structured Covariance Matrices via Mixed-Integer Programming; H. Corrada Bravo, S. Wright, K. Eng, S. Keles, G. Wahba |
| | Covariance Operator Based Dimensionality Reduction with Extension to Semi-Supervised Settings; M. Kim, V. Pavlovic |
| | Learning a Parametric Embedding by Preserving Local Structure; L. van der Maaten |
| | Gaussian Margin Machines; K. Crammer, M. Mohri, F. Pereira |
| | Latent Variable Models for Dimensionality Reduction; Z. zhang, M. Jordan |
| | Tree-Based Inference for Dirichlet Process Mixtures; Y. Xu, K. Heller, Z. Ghahramani |
| | Sleeping Experts and Bandits with Stochastic Action Availability and Adversarial Rewards; V. Kanade, H. McMahan, B. Bryan |
| | Active Learning as Non-Convex Optimization; A. Guillory, E. Chastain, J. Bilmes |
| | Particle Belief Propagation; A. Ihler, D. McAllester |
| | Semi-Supervised Affinity Propagation with Instance-Level Constraints; I. Givoni, B. Frey |
| | Multi-Manifold Semi-Supervised Learning; A. Goldberg, X. Zhu, A. Singh, Z. Xu, R. Nowak |
| | Latent Wishart Processes for Relational Kernel Learning; W. Li, Z. zhang, D. Yeung |
| | Novelty detection: Unlabeled data definitely help; C. Scott, G. Blanchard |
| | Kernel Learning by Unconstrained Optimization; F. Li, Y. Fu, Y. Dai, C. Sminchisescu, W. jue |
| | The Difficulty of Training Deep Architectures and the Effect of Unsupervised Pre-Training; D. Erhan, P. Manzagol, Y. Bengio, S. Bengio, P. Vincent |
| | Learning Low Density Separators; D. Pal, S. Ben-David, T. Lu, M. Sotakova |
| Poster Session III - Saturday 3:00pm-6:00pm |
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| | Factorial Mixture of Gaussians and the Marginal Independence Model; R. Silva, Z. Ghahramani |
| | Non-Negative Semi-Supervised Learning; C. Wang, S. Yan, L. Zhang, H. Zhang |
| | Supervised Spectral Latent Variable Models; L. Bo, C. Sminchisescu |
| | Relative Novelty Detection; A. Smola, L. Song, C. Teo |
| | Sparse Probabilistic Principal Component Analysis; G. Yue, J. Dy |
| | PAC-Bayes Analysis Of Maximum Entropy Learning; J. Shawe-Taylor, D. Hardoon |
| | Learning Sparse Markov Network Structure via Ensemble-of-Trees Models; Y. Lin, S. Zhu, D. Lee, B. Taskar |
| | Speed and Sparsity of Regularized Boosting; Y. Xi, Z. Xiang, P. Ramadge, R. Schapire |
| | Deep Boltzmann Machines; R. Salakhutdinov, G. Hinton |
| | Sequential Learning of Classifiers for Structured Prediction Problems; D. Roth, K. Small, I. Titov |
| | Infinite Hierarchical Hidden Markov Models; K. Heller, Y. Teh, D. Gorur |
| | Deep Learning using Robust Interdependent Codes; H. Larochelle, D. Erhan, P. Vincent |
| | Matching Pursuit Kernel Fisher Discriminant Analysis; T. Diethe, Z. Hussain, D. Hardoon, J. Shawe-Taylor |
| | Locally Minimax Optimal Predictive Modeling with Bayesian Networks; T. Silander, T. Roos, P. Myllymaki |
| | Distilled sensing: selective sampling for sparse signal recovery; J. Haupt, R. Castro, R. Nowak |
| | Residual Splash for Optimally Parallelizing Belief Propagation; J. Gonzalez, Y. Low, C. Guestrin |
| | Dual Temporal Difference Learning; M. Yang, Y. Li, D. Schuurmans |
| | Choosing a Variable to Clamp; F. Eaton, Z. Ghahramani |
| | An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Reward; M. Hoffman, N. de Freitas, A. Doucet, J. Peters |
| | Online Inference of Topics with Latent Dirichlet Allocation; K. Canini, L. Shi, T. Griffiths |
