JMLR - Proceedings Track, Volume 15

Volume 15, June 2011

• Geoffrey J. Gordon, David B. Dunson:
  Preface. 1-2
  
• Robert E. Tillman, Peter Spirtes:
  Learning equivalence classes of acyclic models with latent and selection variables from multiple datasets with overlapping variables. 3-15
  
• Jiji Zhang, Ricardo Silva:
  Discussion of "Learning Equivalence Classes of Acyclic Models with Latent and Selection Variables from Multiple Datasets with Overlapping Variables". 16-18
  
• Alina Beygelzimer, John Langford, Lihong Li, Lev Reyzin, Robert E. Schapire:
  Contextual Bandit Algorithms with Supervised Learning Guarantees. 19-26
  
• H. Brendan McMahan:
  Discussion of "Contextual Bandit Algorithms with Supervised Learning Guarantees". 27-28
  
• Hugo Larochelle, Iain Murray:
  The Neural Autoregressive Distribution Estimator. 29-37
  
• Yoshua Bengio:
  Discussion of "The Neural Autoregressive Distribution Estimator". 38-39
  
• Qiang Liu, Alexander T. Ihler:
  Learning Scale Free Networks by Reweighted L1 regularization. 40-48
  
• Deepak Agarwal:
  Discussion of "Learning Scale Free Networks by Reweighted L1 regularization". 49-50
  
• Neil D. Lawrence:
  Spectral Dimensionality Reduction via Maximum Entropy. 51-59
  
• Laurens van der Maaten:
  Discussion of "Spectral Dimensionality Reduction via Maximum Entropy". 60-62
  
• Frederik Eaton:
  A conditional game for comparing approximations. 63-71
  
• Vincent Conitzer:
  Discussion of "A conditional game for comparing approximations". 72-73
  
• John William Paisley, Chong Wang, David M. Blei:
  The Discrete Infinite Logistic Normal Distribution for Mixed-Membership Modeling. 74-82
  
• Frank Wood:
  Discussion of "The Discrete Infinite Logistic Normal Distribution for Mixed-Membership Modeling". 83-84
  
• Arvind Agarwal, Hal Daumé III:
  Generative Kernels for Exponential Families. 85-92
  
• Deepak Agarwal, Lihong Li, Alexander J. Smola:
  Linear-Time Estimators for Propensity Scores. 93-100
  
• Amr Ahmed, Qirong Ho, Choon Hui Teo, Jacob Eisenstein, Alexander J. Smola, Eric P. Xing:
  Online Inference for the Infinite Topic-Cluster Model: Storylines from Streaming Text. 101-109
  
• Edoardo Airoldi, Bertrand Haas:
  Polytope samplers for inference in ill-posed inverse problems. 110-118
  
• Mohammad Gheshlaghi Azar, Vicenç Gómez, Bert Kappen:
  Dynamic Policy Programming with Function Approximation. 119-127
  
• Anoop Korattikara Balan, Levi Boyles, Max Welling, Jingu Kim, Haesun Park:
  Statistical Optimization of Non-Negative Matrix Factorization. 128-136
  
• Krishnakumar Balasubramanian, Pinar Donmez, Guy Lebanon:
  Unsupervised Supervised Learning II: Margin-Based Classification without Labels. 137-145
  
• Dhruv Batra, Sebastian Nowozin, Pushmeet Kohli:
  Tighter Relaxations for MAP-MRF Inference: A Local Primal-Dual Gap based Separation Algorithm. 146-154
  
• Gowtham Bellala, Suresh K. Bhavnani, Clayton Scott:
  Active Diagnosis under Persistent Noise with Unknown Noise Distribution: A Rank-Based Approach. 155-163
  
• Yoshua Bengio, Frédéric Bastien, Arnaud Bergeron, Nicolas Boulanger-Lewandowski, Thomas M. Breuel, Youssouf Chherawala, Moustapha Cisse, Myriam Côté, Dumitru Erhan, Jeremy Eustache, Xavier Glorot, Xavier Muller, Sylvain Pannetier Lebeuf, Razvan Pascanu, Salah Rifai, François Savard, Guillaume Sicard:
  Deep Learners Benefit More from Out-of-Distribution Examples. 164-172
  
• John Blitzer, Sham Kakade, Dean P. Foster:
  Domain Adaptation with Coupled Subspaces. 173-181
  
• Abdeslam Boularias, Jens Kober, Jan Peters:
  Relative Entropy Inverse Reinforcement Learning. 182-189
  
• Chris Bracegirdle, David Barber:
  Switch-Reset Models : Exact and Approximate Inference. 190-198
  
• Edward Challis, David Barber:
  Concave Gaussian Variational Approximations for Inference in Large-Scale Bayesian Linear Models. 199-207
  
• Wei Chu, Lihong Li, Lev Reyzin, Robert E. Schapire:
  Contextual Bandits with Linear Payoff Functions. 208-214
  
• Adam Coates, Andrew Y. Ng, Honglak Lee:
  An Analysis of Single-Layer Networks in Unsupervised Feature Learning. 215-223
  
• Ronan Collobert:
  Deep Learning for Efficient Discriminative Parsing. 224-232
  
• Aaron C. Courville, James Bergstra, Yoshua Bengio:
  A Spike and Slab Restricted Boltzmann Machine. 233-241
  
• Christopher R. Dance, Onno Zoeter:
  Optimal and Robust Price Experimentation: Learning by Lottery. 242-250
  
• Gal Elidan:
  Bagged Structure Learning of Bayesian Network. 251-259
  
• Brian Eriksson, Gautam Dasarathy, Aarti Singh, Robert D. Nowak:
  Active Clustering: Robust and Efficient Hierarchical Clustering using Adaptively Selected Similarities. 260-268
  
• Ahmed K. Farahat, Ali Ghodsi, Mohamed S. Kamel:
  A novel greedy algorithm for Nyström approximation. 269-277
  
• James R. Foulds, Nicholas Navaroli, Padhraic Smyth, Alexander T. Ihler:
  Revisiting MAP Estimation, Message Passing and Perfect Graphs. 278-286
  
• James R. Foulds, Christopher DuBois, Arthur U. Asuncion, Carter T. Butts, Padhraic Smyth:
  A Dynamic Relational Infinite Feature Model for Longitudinal Social Networks. 287-295
  
• Rahul Garg, Rohit Khandekar:
  Block-sparse Solutions using Kernel Block RIP and its Application to Group Lasso. 296-304
  
• Pierre Geurts:
  Learning from positive and unlabeled examples by enforcing statistical significance. 305-314
  
• Xavier Glorot, Antoine Bordes, Yoshua Bengio:
  Deep Sparse Rectifier Neural Networks. 315-323
  
• Joseph Gonzalez, Yucheng Low, Arthur Gretton, Carlos Guestrin:
  Parallel Gibbs Sampling: From Colored Fields to Thin Junction Trees. 324-332
  
• Qirong Ho, Ankur P. Parikh, Le Song, Eric P. Xing:
  Multiscale Community Blockmodel for Network Exploration. 333-341
  
• Qirong Ho, Le Song, Eric P. Xing:
  Evolving Cluster Mixed-Membership Blockmodel for Time-Evolving Networks. 342-350
  
• Sue Ann Hong, Geoffrey J. Gordon:
  Optimal Distributed Market-Based Planning for Multi-Agent Systems with Shared Resources. 351-360
  
• Bert C. Huang, Tony Jebara:
  Fast b-matching via Sufficient Selection Belief Propagation. 361-369
  
• Zakria Hussain, John Shawe-Taylor:
  Improved Loss Bounds For Multiple Kernel Learning. 370-377
  
• Ali Jalali, Pradeep D. Ravikumar, Vishvas Vasuki, Sujay Sanghavi:
  On Learning Discrete Graphical Models using Group-Sparse Regularization. 378-387
  
• Jeremy Jancsary, Gerald Matz:
  Convergent Decomposition Solvers for Tree-reweighted Free Energies. 388-398
  
• Vladimir Jojic, Suchi Saria, Daphne Koller:
  Convex envelopes of complexity controlling penalties: the case against premature envelopment. 399-406
  
• Mladen Kolar, Eric P. Xing:
  On Time Varying Undirected Graphs. 407-415
  
• Simon Lacoste-Julien, Ferenc Huszar, Zoubin Ghahramani:
  Approximate inference for the loss-calibrated Bayesian. 416-424
  
• Balaji Lakshminarayanan, Guillaume Bouchard, Cédric Archambeau:
  Robust Bayesian Matrix Factorisation. 425-433
  
• Bin Li, Steven C. H. Hoi, Peilin Zhao, Vivekanand Gopalkrishnan:
  Confidence Weighted Mean Reversion Strategy for On-Line Portfolio Selection. 434-442
  
• Dazhuo Li, Patrick Shafto:
  Bayesian Hierarchical Cross-Clustering. 443-451
  
• Aurelie C. Lozano, Grzegorz Swirszcz, Naoki Abe:
  Group Orthogonal Matching Pursuit for Logistic Regression. 452-460
  
• Hongtao Lu, Xianzhong Long, Jingyuan Lv:
  A Fast Algorithm for Recovery of Jointly Sparse Vectors based on the Alternating Direction Methods. 461-469
  
• Heng Luo, Ruimin Shen, Changyong Niu, Carsten Ullrich:
  Learning Class-relevant Features and Class-irrelevant Features via a Hybrid third-order RBM. 470-478
  
• Laurens van der Maaten, Max Welling, Lawrence K. Saul:
  Hidden-Unit Conditional Random Fields. 479-488
  
• Satyaki Mahalanabis:
  Learning mixtures of Gaussians with maximum-a-posteriori oracle. 489-497
  
• Ravi Sastry Ganti Mahapatruni, Alexander G. Gray:
  CAKE: Convex Adaptive Kernel Density Estimation. 498-506
  
• André Filipe Torres Martins, Noah A. Smith, Eric P. Xing, Pedro M. Q. Aguiar, Mário A. T. Figueiredo:
  Online Learning of Structured Predictors with Multiple Kernels. 507-515
  
• Daniel J. McDonald, Cosma Rohilla Shalizi, Mark J. Schervish:
  Estimating beta-mixing coefficients. 516-524
  
• H. Brendan McMahan:
  Follow-the-Regularized-Leader and Mirror Descent: Equivalence Theorems and L1 Regularization. 525-533
  
• Mehryar Mohri, Ameet Talwalkar:
  Can matrix coherence be efficiently and accurately estimated? 534-542
  
• Ramesh Nallapati, Daniel A. McFarland, Christopher D. Manning:
  TopicFlow Model: Unsupervised Learning of Topic-specific Influences of Hyperlinked Documents. 543-551
  
• Donglin Niu, Jennifer G. Dy, Michael I. Jordan:
  Dimensionality Reduction for Spectral Clustering. 552-560
  
• Gang Niu, Bo Dai, Lin Shang, Masashi Sugiyama:
  Maximum Volume Clustering. 561-569
  
• Odalric-Ambrym Maillard, Rémi Munos:
  Adaptive Bandits: Towards the best history-dependent strategy. 570-578
  
• Jaakko Peltonen, Samuel Kaski:
  Generative Modeling for Maximizing Precision and Recall in Information Visualization. 579-587
  
• Jose M. Peña:
  Faithfulness in Chain Graphs: The Gaussian Case. 588-599
  
• Sergey M. Plis, Stephen McCracken, Terran Lane, Vince D. Calhoun:
  Directional Statistics on Permutations. 600-608
  
• Barnabás Póczos, Jeff G. Schneider:
  On the Estimation of alpha-Divergences. 609-617
  
• Pradeep D. Ravikumar, Ambuj Tewari, Eunho Yang:
  On NDCG Consistency of Listwise Ranking Methods. 618-626
  
• Stéphane Ross, Geoffrey J. Gordon, Drew Bagnell:
  A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning. 627-635
  
• Ankan Saha, Ambuj Tewari:
  Improved Regret Guarantees for Online Smooth Convex Optimization with Bandit Feedback. 636-642
  
• Avishek Saha, Piyush Rai, Hal Daumé III, Suresh Venkatasubramanian:
  Online Learning of Multiple Tasks and Their Relationships. 643-651
  
• Matthias W. Seeger, Hannes Nickisch:
  Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference. 652-660
  
• Ohad Shamir, Naftali Tishby:
  Spectral Clustering on a Budget. 661-669
  
• Ricardo Silva, Charles Blundell, Yee Whye Teh:
  Mixed Cumulative Distribution Networks. 670-678
  
• Mathieu Sinn, Pascal Poupart:
  Asymptotic Theory for Linear-Chain Conditional Random Fields. 679-687
  
• Ning Situ, Xiaojing Yuan, George Zouridakis:
  Assisting Main Task Learning by Heterogeneous Auxiliary Tasks with Applications to Skin Cancer Screening. 688-697
  
• Richard Socher, Andrew L. Maas, Christopher D. Manning:
  Spectral Chinese Restaurant Processes: Nonparametric Clustering Based on Similarities. 698-706
  
• Le Song, Arthur Gretton, Danny Bickson, Yucheng Low, Carlos Guestrin:
  Kernel Belief Propagation. 707-715
  
• Amos J. Storkey:
  Machine Learning Markets. 716-724
  
• Veselin Stoyanov, Alexander Ropson, Jason Eisner:
  Empirical Risk Minimization of Graphical Model Parameters Given Approximate Inference, Decoding, and Model Structure. 725-733
  
• Mingxuan Sun, Guy Lebanon, Paul Kidwell:
  Estimating Probabilities in Recommendation Systems. 734-742
  
• Kirill Trapeznikov, Venkatesh Saligrama, David A. Castañon:
  Active Boosted Learning (ActBoost). 743-751
  
• Chong Wang, John William Paisley, David M. Blei:
  Online Variational Inference for the Hierarchical Dirichlet Process. 752-760
  
• Meihong Wang, Fei Sha:
  Information Theoretical Clustering via Semidefinite Programming. 761-769
  
• David Wingate, Andreas Stuhlmüller, Noah D. Goodman:
  Lightweight Implementations of Probabilistic Programming Languages Via Transformational Compilation. 770-778
  
• Rongjing Xiang, Jennifer Neville:
  Relational Learning with One Network: An Asymptotic Analysis. 779-788
  
• Liang Xiong, Barnabás Póczos, Jeff G. Schneider, Andrew Connolly, Jake VanderPlas:
  Hierarchical Probabilistic Models for Group Anomaly Detection. 789-797
  
• Tianbing Xu, Alexander T. Ihler:
  Multicore Gibbs Sampling in Dense, Unstructured Graphs. 798-806
  
• Makoto Yamada, Masashi Sugiyama:
  Cross-Domain Object Matching with Model Selection. 807-815
  
• Liu Yang, Steve Hanneke, Jaime G. Carbonell:
  The Sample Complexity of Self-Verifying Bayesian Active Learning. 816-822
  
• Shuang-Hong Yang, Steven P. Crain, Hongyuan Zha:
  Bridging the Language Gap: Topic Adaptation for Documents with Different Technicality. 823-831
  
• Gui-Bo Ye, Yifei Chen, Xiaohui Xie:
  Efficient variable selection in support vector machines via the alternating direction method of multipliers. 832-840
  
• Xiaotong Yuan, Shuicheng Yan:
  A Finite Newton Algorithm for Non-degenerate Piecewise Linear Systems. 841-854
  
• Erik Zawadzki, Geoffrey J. Gordon, André Platzer:
  An Instantiation-Based Theorem Prover for First-Order Programming. 855-863
  
• Chao Zhang, Dacheng Tao:
  Generalization Bound for Infinitely Divisible Empirical Process. 864-872
  
• Yi Zhang, Jeff G. Schneider:
  Multi-Label Output Codes using Canonical Correlation Analysis. 873-882
  
• Mingyuan Zhou, Hongxia Yang, Guillermo Sapiro, David B. Dunson, Lawrence Carin:
  Dependent Hierarchical Beta Process for Image Interpolation and Denoising. 883-891
  
• Xueyuan Zhou, Mikhail Belkin:
  Semi-supervised Learning by Higher Order Regularization. 892-900
  
• Xueyuan Zhou, Nathan Srebro:
  Error Analysis of Laplacian Eigenmaps for Semi-supervised Learning. 901-908
  
• Jinfeng Zhuang, Ivor W. Tsang, Steven C. H. Hoi:
  Two-Layer Multiple Kernel Learning. 909-917
  

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