A Bayesian Divergence Prior for Classifier Adaptation [Xiao Li and Jeff Bilmes] A Boosting Algorithm for Label Covering in Multilabel Problems [Yonatan Amit, Ofer Dekel, Yoram Singer] AClass: A simple, online, parallelizable algorithm for probabilistic classification [Vikash Mansinghka, Daniel Roy, Ryan Rifkin, and Josh Tenenbaum] A fast algorithm for learning large scale preference relations [Vikas Raykar, Ramani Duraiswami, and Balaji Krishnapuram] A Fast Bundle-based Anytime Algorithm for Poker and other Convex Games [H. Brendan McMahan and Geoffrey Gordon] A Framework for Probability Density Estimation [John Shawe-Taylor and Alex Dolia] A Hybrid Pareto Model for Conditional Density Estimation of Asymmetric Fat-Tail Data [Julie Carreau and Yoshua Bengio] A Latent Space Approach to Dynamic Embedding of Co-occurrence Data [Purnamrita Sarkar, Sajid Siddiqi, and Geoff Gordon] Analogical Reasoning with Relational Bayesian Sets [Ricardo Silva, Katherine Heller, and Zoubin Ghahramani] An Improved 1-norm SVM for Simultaneous Classification and Variable Selection [Hui Zou] A Nonparametric Bayesian Approach to Modeling Overlapping Clusters [Katherine Heller and Zoubin Ghahramani] Approximate Counting of Graphical Models Via MCMC [Jose M Pena] Approximate inference using conditional entropy decompositions [Amir Globerson and Tommi Jaakkola] (Approximate) Subgradient Methods for Structured Prediction [Nathan Ratliff, J. Andrew Bagnell, and Martin Zinkevich] A Stochastic Quasi-Newton Method for Online Convex Optimization [Nic Schraudolph, Jin Yu, and Simon Guenter] A Unified Algorithmic Approach for Efficient Online Label Ranking [Shai Shalev-Shwartz, and Yoram Singer] A unified energy-based framework for unsupervised learning [Marc'Aurelio Ranzato, Y-Lan Boureau, Sumit Chopra, and Yann LeCun] Bayesian Inference and Optimal Design in the Sparse Linear Model [Matthias Seeger, Florian Steinke, and Koji Tsuda] Continuous Neural Networks [Nicolas Le Roux and Yoshua Bengio] Deterministic Annealing for Multiple-Instance Learning [Peter Gehler and Olivier Chapelle] Dissimilarity in Graph-Based Semi-Supervised Classification [Andrew Goldberg, Xiaojin Zhu, and Steve Wright] Dynamic Factorization Tests: Applications to Multi-modal Data Association [Michael Siracusa and John Fisher] Efficient active learning with generalized linear models [Jeremy Lewi, Robert Butera, and Liam Paninski] Efficient large margin semisupervised learning [Junhui Wang] Ellipsoidal Machines [Pannagadatta Shivaswamy, and Tony Jebara] Emerge and spread models and word burstiness [Peter Sunehag] Exact Bayesian structure learning from uncertain interventions [Daniel Eaton and Kevin Murphy] Fast Kernel ICA using an Approximate Newton Method [Hao Shen, Stefanie Jegelka, and Arthur Gretton] Fast Low-Rank Semidefinite Programming for Embedding and Clustering [Brian Kulis, Arun Surendran, and John C. Platt] Fast Mean Shift with Accurate and Stable Convergence [Ping Wang, Dongryeol Lee, Alexander Gray, and James Rehg] Fast search for Dirichlet process mixture models [Hal Daume] Fast State Discovery for HMM Model Selection and Learning [Sajid Siddiqi, Geoff Gordon, and Andrew Moore] Fisher Consistency of Multicategory Support Vector Machines [Yufeng Liu] Generalized Darting Monte Carlo [Cristian Sminchisescu, Max Welling] Generalized Do-Calculus with Testable Causal Assumptions [Jiji Zhang] Generalized Non-metric Multidimensional Scaling [sameer agarwal, Josh Wills, Lawrence Cayton, Gert Lanckriet, Serge Belongie, and David Kriegman] Hidden Topic Markov Models [Amit Gruber, Michal Rosen-Zvi, and Yair Weiss] Hierarchical Beta Processes and the Indian Buffet Process [Romain Thibaux and Michael I. Jordan] How Powerful Can Any Regression Learning Procedure Be? [Yuhong Yang] Incorporating Prior Knowledge on Features into Learning [Eyal Krupka and Naftali Tishby] Inductive Transfer for Bayesian Network Structure Learning [Alexandru Niculescu-Mizil and Rich Caruana] Information Retrieval by Inferring Implicit Queries from Eye Movements [David R. Hardoon, John Shawe-Taylor, Antti Ajanki, Kai Puolamaki, and Samuel Kaski] Kernel Multi-task Learning using Task-specific Features [Edwin Bonilla, Felix Agakov, Chris Williams] Large-Margin Classification in Banach Spaces [Ricky Der and Daniel Lee] Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure [Ruslan Salakhutdinov and Geoffrey Hinton] Learning A* underestimates : Using inference to guide inference [Greg Druck, Mukund Narasimhan, and Paul Viola] Learning for Larger Datasets with the Gaussian Process Latent Variable Model [Neil Lawrence] Learning Markov Structure by Maximum Entropy Relaxation [Jason Johnson, Venkat Chandrasekaran, and Alan Willsky] Learning Multilevel Distributed Representations for High Dimensional Sequences [Ilya Sutskever and Geoffrey Hinton] Learning Nearest-Neighbor Quantizers from Labeled Data by Information Loss Minimization [Svetlana Lazebnik and Maxim Raginsky] Local and global sparse Gaussian process approximations [Edward Snelson, Zoubin Ghahramani] Loop Corrected Belief Propagation [Joris Mooij, bastian wemmenhove, Bert Kappen, and Tommaso Rizzo] Loopy Belief Propagation for Bipartite Maximum Weight b-Matching [Bert Huang and Tony Jebara] Margin based Transductive Graph Cuts using Linear Programming [Kristiaan Pelckmans, John Shawe-Taylor, Johan Suykens, and Bart De Moor] Maximum Entropy Correlated Equilibra [Luis Ortiz, Robert Schapire, and Sham Kakade] MDL Histogram Density Estimation [Petri Kontkanen, Petri Myllymaki] Memory-Efficient Orthogonal Least Squares Kernel Density Estimation using Enhanced Empirical Cumulative Distribution Functions [Martin Schafföner, Edin Andelic, Marcel Katz, Sven Krüger and Andreas Wendemuth] Metric Learning for Kernel Regression [Kilian Weinberger and Gerald Tesauro] Minimum Volume Embedding [Blake Shaw and Tony Jebara] Mixture of Watson Distributions: A Generative Model for Hyperspherical Embeddings [Avleen Bijral, Markus Breitenbach and Greg Grudic] Multi-object tracking with representations of the symmetric group [Risi Kondor, Andrew Howard, and Tony Jebara] Nonlinear Dimensionality Reduction as Information Retrieval [Venna Jarkko and Samuel Kaski] Nonnegative Garrote Component Selection in Functional ANOVA Models [Ming Yuan] Online Learning of Search Heuristics [Michael Fink] On robust stochastic simulation [Pablo Fierens] Performance Guarantees for Information Theoretic Active Inference [Jason Williams, John Fisher, and Alan Willsky] Policy-Gradients for PSRs and POMDPs [Douglas Aberdeen, Olivier Buffet, and Owen Thomas] Predictive Discretization during Model Selection [Harald Steck and Tommi Jaakkola] Recall Systems: Efficient Learning and Use of Category Indices [Omid Madani, Wiley Greiner, David Kempe, and Mohammad Salavatipour] SampleSearch: A Scheme that Searches for Consistent Samples [Vibhav Gogate and Rina Dechter] Seeking The Truly Correlated Topic Posterior -- on tight approximate inference of logistic-normal admixture model [Amr Ahmed and Eric Xing] Semi-supervised Clustering with Pairwise Constraints: A Discriminative Approach [Zhengdong Lu] Semi-Supervised Mean Fields [Fei Wang, Shijun Wang, Changshui Zhang, and Ole Winther] Solving Markov Random Fields with Spectral Relaxation [Timothee Cour and Jianbo Shi] Space-Efficient Sampling [Sudipto Guha, Andrew McGregor] Sparse Nonparametric Density Estimation in High Dimensions Using the Rodeo [Han Liu, John Lafferty, and Larry Wasserman] Stick-breaking Construction for the Indian Buffet Process [Yee Whye Teh, Dilan Gorur, and Zoubin Ghahramani] SVM versus Least Squares SVM [Jieping Ye and Tao Xiong] The Kernel Path in Kernelized LASSO [Gang Wang, Dit-Yan Yeung, and Frederick Lochovsky] The Laplacian Eigenmaps Latent Variable Model [Miguel Carreira-Perpinan, and Zhengdong Lu] Transductive Classification via Local Learning Regularization [Mingrui Wu and Bernhard Schoelkopf] Treelets --- A Tool for Dimensionality Reduction and Multi-Scale Analysis of Unstructured Data [Ann Lee and Boaz Nadler] Visualizing pairwise similarity via semidefinite programming [Amir Globerson and Sam Roweis] Visualizing Similarity Data with a Mixture of Maps [James Cook, Ilya Sutskever, Andriy Mnih, and Geoffrey Hinton]