27. ICML 2010: Haifa, Israel

• Chid Apté:
  The Role of Machine Learning in Business Optimization. 1-2
  
• Mark Joseph Cummins, Paul M. Newman:
  FAB-MAP: Appearance-Based Place Recognition and Mapping using a Learned Visual Vocabulary Model. 3-10
  
• Pedro F. Felzenszwalb, Ross B. Girshick, David A. McAllester, Deva Ramanan:
  Discriminative Latent Variable Models for Object Detection. 11-12
  
• Thore Graepel, Joaquin Quiñonero Candela, Thomas Borchert, Ralf Herbrich:
  Web-Scale Bayesian Click-Through rate Prediction for Sponsored Search Advertising in Microsoft's Bing Search Engine. 13-20
  
• Christopher Raphael:
  Music Plus One and Machine Learning. 21-28
  
• Benjamin Snyder, Regina Barzilay:
  Climbing the Tower of Babel: Unsupervised Multilingual Learning. 29-36
  
• Wei Xu, Ling Huang, Armando Fox, David A. Patterson, Michael I. Jordan:
  Detecting Large-Scale System Problems by Mining Console Logs. 37-46
  
• Arthur U. Asuncion, Qiang Liu, Alexander T. Ihler, Padhraic Smyth:
  Particle Filtered MCMC-MLE with Connections to Contrastive Divergence. 47-54
  
• Rémi Bardenet, Balázs Kégl:
  Surrogating the surrogate: accelerating Gaussian-process-based global optimization with a mixture cross-entropy algorithm. 55-62
  
• Nicholas Bartlett, David Pfau, Frank Wood:
  Forgetting Counts: Constant Memory Inference for a Dependent Hierarchical Pitman-Yor Process. 63-70
  
• Sahely Bhadra, Sourangshu Bhattacharya, Chiranjib Bhattacharyya, Aharon Ben-Tal:
  Robust Formulations for Handling Uncertainty in Kernel Matrices. 71-78
  
• Mustafa Bilgic, Lilyana Mihalkova, Lise Getoor:
  Active Learning for Networked Data. 79-86
  
• David M. Blei, Peter Frazier:
  Distance dependent Chinese restaurant processes. 87-94
  
• Gianluca Bontempi, Patrick Emmanuel Meyer:
  Causal filter selection in microarray data. 95-102
  
• Antoine Bordes, Nicolas Usunier, Jason Weston:
  Label Ranking under Ambiguous Supervision for Learning Semantic Correspondences. 103-110
  
• Y-Lan Boureau, Jean Ponce, Yann LeCun:
  A Theoretical Analysis of Feature Pooling in Visual Recognition. 111-118
  
• Bruno Bouzy, Marc Métivier:
  Multi-agent Learning Experiments on Repeated Matrix Games. 119-126
  
• Joseph K. Bradley, Carlos Guestrin:
  Learning Tree Conditional Random Fields. 127-134
  
• Nader H. Bshouty, Philip M. Long:
  Finding Planted Partitions in Nearly Linear Time using Arrested Spectral Clustering. 135-142
  
• Róbert Busa-Fekete, Balázs Kégl:
  Fast boosting using adversarial bandits. 143-150
  
• Kevin Robert Canini, Mikhail M. Shashkov, Thomas L. Griffiths:
  Modeling Transfer Learning in Human Categorization with the Hierarchical Dirichlet Process. 151-158
  
• Bin Cao, Nathan Nan Liu, Qiang Yang:
  Transfer Learning for Collective Link Prediction in Multiple Heterogenous Domains. 159-166
  
• Miguel Á. Carreira-Perpiñán:
  The Elastic Embedding Algorithm for Dimensionality Reduction. 167-174
  
• Nicolò Cesa-Bianchi, Claudio Gentile, Fabio Vitale, Giovanni Zappella:
  Random Spanning Trees and the Prediction of Weighted Graphs. 175-182
  
• Nicolò Cesa-Bianchi, Shai Shalev-Shwartz, Ohad Shamir:
  Efficient Learning with Partially Observed Attributes. 183-190
  
• Doran Chakraborty, Peter Stone:
  Convergence, Targeted Optimality, and Safety in Multiagent Learning. 191-198
  
• Ming-Wei Chang, Vivek Srikumar, Dan Goldwasser, Dan Roth:
  Structured Output Learning with Indirect Supervision. 199-206
  
• Yutian Chen, Max Welling:
  Dynamical Products of Experts for Modeling Financial Time Series. 207-214
  
• Weiwei Cheng, Krzysztof Dembczynski, Eyke Hüllermeier:
  Label Ranking Methods based on the Plackett-Luce Model. 215-222
  
• Weiwei Cheng, Krzysztof Dembczynski, Eyke Hüllermeier:
  Graded Multilabel Classification: The Ordinal Case. 223-230
  
• Michael H. Coen, M. Hidayath Ansari, Nathanael Fillmore:
  Comparing Clusterings in Space. 231-238
  
• Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh:
  Two-Stage Learning Kernel Algorithms. 239-246
  
• Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh:
  Generalization Bounds for Learning Kernels. 247-254
  
• Fabrizio Costa, Kurt De Grave:
  Fast Neighborhood Subgraph Pairwise Distance Kernel. 255-262
  
• Sajib Dasgupta, Vincent Ng:
  Mining Clustering Dimensions. 263-270
  
• Jesse Davis, Pedro Domingos:
  Bottom-Up Learning of Markov Network Structure. 271-278
  
• Krzysztof Dembczynski, Weiwei Cheng, Eyke Hüllermeier:
  Bayes Optimal Multilabel Classification via Probabilistic Classifier Chains. 279-286
  
• Thomas Deselaers, Vittorio Ferrari:
  A Conditional Random Field for Multiple-Instance Learning. 287-294
  
• Joshua V. Dillon, Krishnakumar Balasubramanian, Guy Lebanon:
  Asymptotic Analysis of Generative Semi-Supervised Learning. 295-302
  
• Frank Dondelinger, Sophie Lebre, Dirk Husmeier:
  Heterogeneous Continuous Dynamic Bayesian Networks with Flexible Structure and Inter-Time Segment Information Sharing. 303-310
  
• Carlton Downey, Scott Sanner:
  Temporal Difference Bayesian Model Averaging: A Bayesian Perspective on Adapting Lambda. 311-318
  
• Gregory Druck, Andrew McCallum:
  High-Performance Semi-Supervised Learning using Discriminatively Constrained Generative Models. 319-326
  
• John C. Duchi, Lester W. Mackey, Michael I. Jordan:
  On the Consistency of Ranking Algorithms. 327-334
  
• Krishnamurthy Dvijotham, Emanuel Todorov:
  Inverse Optimal Control with Linearly-Solvable MDPs. 335-342
  
• Tal El-Hay, Ido Cohn, Nir Friedman, Raz Kupferman:
  Continuous-Time Belief Propagation. 343-350
  
• Lev Faivishevsky, Jacob Goldberger:
  Nonparametric Information Theoretic Clustering Algorithm. 351-358
  
• Romaric Gaudel, Michèle Sebag:
  Feature Selection as a One-Player Game. 359-366
  
• Matan Gavish, Boaz Nadler, Ronald R. Coifman:
  Multiscale Wavelets on Trees, Graphs and High Dimensional Data: Theory and Applications to Semi Supervised Learning. 367-374
  
• Sean Gerrish, David M. Blei:
  A Language-based Approach to Measuring Scholarly Impact. 375-382
  
• Noam Goldberg, Jonathan Eckstein:
  Boosting Classifiers with Tightened L0-Relaxation Penalties. 383-390
  
• Ryan Gomes, Andreas Krause:
  Budgeted Nonparametric Learning from Data Streams. 391-398
  
• Karol Gregor, Yann LeCun:
  Learning Fast Approximations of Sparse Coding. 399-406
  
• Alexander Grubb, J. Andrew Bagnell:
  Boosted Backpropagation Learning for Training Deep Modular Networks. 407-414
  
• Andrew Guillory, Jeff Bilmes:
  Interactive Submodular Set Cover. 415-422
  
• Bharath Hariharan, Lihi Zelnik-Manor, S. V. N. Vishwanathan, Manik Varma:
  Large Scale Max-Margin Multi-Label Classification with Priors. 423-430
  
• Abhay Harpale, Yiming Yang:
  Active Learning for Multi-Task Adaptive Filtering. 431-438
  
• Matthew D. Hoffman, David M. Blei, Perry R. Cook:
  Bayesian Nonparametric Matrix Factorization for Recorded Music. 439-446
  
• Jean Honorio, Dimitris Samaras:
  Multi-Task Learning of Gaussian Graphical Models. 447-454
  
• Jonathan Huang, Carlos Guestrin:
  Learning Hierarchical Riffle Independent Groupings from Rankings. 455-462
  
• Martial Hue, Jean-Philippe Vert:
  On learning with kernels for unordered pairs. 463-470
  
• Martin Jaggi, Marek Sulovský:
  A Simple Algorithm for Nuclear Norm Regularized Problems. 471-478
  
• Dominik Janzing, Patrik O. Hoyer, Bernhard Schölkopf:
  Telling cause from effect based on high-dimensional observations. 479-486
  
• Rodolphe Jenatton, Julien Mairal, Guillaume Obozinski, Francis Bach:
  Proximal Methods for Sparse Hierarchical Dictionary Learning. 487-494
  
• Shuiwang Ji, Wei Xu, Ming Yang, Kai Yu:
  3D Convolutional Neural Networks for Human Action Recognition. 495-502
  
• Vladimir Jojic, Stephen Gould, Daphne Koller:
  Accelerated dual decomposition for MAP inference. 503-510
  
• Shivaram Kalyanakrishnan, Peter Stone:
  Efficient Selection of Multiple Bandit Arms: Theory and Practice. 511-518
  
• Dongmin Kim, Suvrit Sra, Inderjit S. Dhillon:
  A scalable trust-region algorithm with application to mixed-norm regression. 519-526
  
• Minyoung Kim, Fernando De la Torre:
  Local Minima Embedding. 527-534
  
• Minyoung Kim, Fernando De la Torre:
  Gaussian Processes Multiple Instance Learning. 535-542
  
• Seyoung Kim, Eric P. Xing:
  Tree-Guided Group Lasso for Multi-Task Regression with Structured Sparsity. 543-550
  
• Stanley Kok, Pedro Domingos:
  Learning Markov Logic Networks Using Structural Motifs. 551-558
  
• Mladen Kolar, Ankur P. Parikh, Eric P. Xing:
  On Sparse Nonparametric Conditional Covariance Selection. 559-566
  
• Andreas Krause, Volkan Cevher:
  Submodular Dictionary Selection for Sparse Representation. 567-574
  
• Brian Kulis, Peter L. Bartlett:
  Implicit Online Learning. 575-582
  
• Tobias Lang, Marc Toussaint:
  Probabilistic Backward and Forward Reasoning in Stochastic Relational Worlds. 583-590
  
• Nathan Lay, Adrian Barbu:
  Supervised Aggregation of Classifiers using Artificial Prediction Markets. 591-598
  
• Alessandro Lazaric, Mohammad Ghavamzadeh:
  Bayesian Multi-Task Reinforcement Learning. 599-606
  
• Alessandro Lazaric, Mohammad Ghavamzadeh, Rémi Munos:
  Analysis of a Classification-based Policy Iteration Algorithm. 607-614
  
• Alessandro Lazaric, Mohammad Ghavamzadeh, Rémi Munos:
  Finite-Sample Analysis of LSTD. 615-622
  
• Nicolas Le Roux, Andrew W. Fitzgibbon:
  A fast natural Newton method. 623-630
  
• Mu Li, James T. Kwok, Bao-Liang Lu:
  Making Large-Scale Nyström Approximation Possible. 631-638
  
• Percy Liang, Michael I. Jordan, Dan Klein:
  Learning Programs: A Hierarchical Bayesian Approach. 639-646
  
• Percy Liang, Nati Srebro:
  On the Interaction between Norm and Dimensionality: Multiple Regimes in Learning. 647-654
  
• Frank Lin, William W. Cohen:
  Power Iteration Clustering. 655-662
  
• Guangcan Liu, Zhouchen Lin, Yong Yu:
  Robust Subspace Segmentation by Low-Rank Representation. 663-670
  
• Hairong Liu, Shuicheng Yan:
  Robust Graph Mode Seeking by Graph Shift. 671-678
  
• Wei Liu, Junfeng He, Shih-Fu Chang:
  Large Graph Construction for Scalable Semi-Supervised Learning. 679-686
  
• Yan Liu, Alexandru Niculescu-Mizil, Aurelie C. Lozano, Yong Lu:
  Learning Temporal Causal Graphs for Relational Time-Series Analysis. 687-694
  
• Daniel J. Lizotte, Michael H. Bowling, Susan A. Murphy:
  Efficient Reinforcement Learning with Multiple Reward Functions for Randomized Controlled Trial Analysis. 695-702
  
• Philip M. Long, Rocco A. Servedio:
  Restricted Boltzmann Machines are Hard to Approximately Evaluate or Simulate. 703-710
  
• Lester W. Mackey, David Weiss, Michael I. Jordan:
  Mixed Membership Matrix Factorization. 711-718
  
• Hamid Reza Maei, Csaba Szepesvári, Shalabh Bhatnagar, Richard S. Sutton:
  Toward Off-Policy Learning Control with Function Approximation. 719-726
  
• M. M. Hassan Mahmud:
  Constructing States for Reinforcement Learning. 727-734
  
• James Martens:
  Deep learning via Hessian-free optimization. 735-742
  
• James Martens:
  Learning the Linear Dynamical System with ASOS. 743-750
  
• Mahdokht Masaeli, Glenn Fung, Jennifer G. Dy:
  From Transformation-Based Dimensionality Reduction to Feature Selection. 751-758
  
• Hamed Masnadi-Shirazi, Nuno Vasconcelos:
  Risk minimization, probability elicitation, and cost-sensitive SVMs. 759-766
  
• Julian John McAuley, Tibério S. Caetano:
  Exploiting Data-Independence for Fast Belief-Propagation. 767-774
  
• Brian McFee, Gert R. G. Lanckriet:
  Metric Learning to Rank. 775-782
  
• Ofer Meshi, David Sontag, Tommi Jaakkola, Amir Globerson:
  Learning Efficiently with Approximate Inference via Dual Losses. 783-790
  
• Martin Renqiang Min, Laurens van der Maaten, Zineng Yuan, Anthony J. Bonner, Zhaolei Zhang:
  Deep Supervised t-Distributed Embedding. 791-798
  
• Tetsuro Morimura, Masashi Sugiyama, Hisashi Kashima, Hirotaka Hachiya, Toshiyuki Tanaka:
  Nonparametric Return Distribution Approximation for Reinforcement Learning. 799-806
  
• Vinod Nair, Geoffrey E. Hinton:
  Rectified Linear Units Improve Restricted Boltzmann Machines. 807-814
  
• Shinichi Nakajima, Masashi Sugiyama:
  Implicit Regularization in Variational Bayesian Matrix Factorization. 815-822
  
• Sahand Negahban, Martin J. Wainwright:
  Estimation of (near) low-rank matrices with noise and high-dimensional scaling. 823-830
  
• Donglin Niu, Jennifer G. Dy, Michael I. Jordan:
  Multiple Non-Redundant Spectral Clustering Views. 831-838
  
• Santiago Ontañón, Enric Plaza:
  Multiagent Inductive Learning: an Argumentation-based Approach. 839-846
  
• John William Paisley, Aimee K. Zaas, Christopher W. Woods, Geoffrey S. Ginsburg, Lawrence Carin:
  A Stick-Breaking Construction of the Beta Process. 847-854
  
• Constantinos Panagiotakopoulos, Petroula Tsampouka:
  The Margin Perceptron with Unlearning. 855-862
  
• David Pardoe, Peter Stone:
  Boosting for Regression Transfer. 863-870
  
• Marek Petrik, Gavin Taylor, Ronald Parr, Shlomo Zilberstein:
  Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes. 871-878
  
• Liuyang Li, Barnabás Póczos, Csaba Szepesvári, Russell Greiner:
  Budgeted Distribution Learning of Belief Net Parameters. 879-886
  
• Leonard K. M. Poon, Nevin Lianwen Zhang, Tao Chen, Yi Wang:
  Variable Selection in Model-Based Clustering: To Do or To Facilitate. 887-894
  
• Monica Dinculescu, Doina Precup:
  Approximate Predictive Representations of Partially Observable Systems. 895-902
  
• Joseph Reisinger, Austin Waters, Bryan Silverthorn, Raymond J. Mooney:
  Spherical Topic Models. 903-910
  
• Stefan Rüping:
  SVM Classifier Estimation from Group Probabilities. 911-918
  
• Daniil Ryabko:
  Clustering processes. 919-926
  
• Yunus Saatci, Ryan Turner, Carl Edward Rasmussen:
  Gaussian Process Change Point Models. 927-934
  
• Jun Sakuma, Hiromi Arai:
  Online Prediction with Privacy. 935-942
  
• Ruslan Salakhutdinov:
  Learning Deep Boltzmann Machines using Adaptive MCMC. 943-950
  
• Christoph Sawade, Niels Landwehr, Steffen Bickel, Tobias Scheffer:
  Active Risk Estimation. 951-958
  
• Bruno Scherrer:
  Should one compute the Temporal Difference fix point or minimize the Bellman Residual? The unified oblique projection view. 959-966
  
• Matthias W. Seeger:
  Gaussian Covariance and Scalable Variational Inference. 967-974
  
• Ali H. Shoeb, John V. Guttag:
  Application of Machine Learning To Epileptic Seizure Detection. 975-982
  
• Aleksandrs Slivkins, Filip Radlinski, Sreenivas Gollapudi:
  Learning optimally diverse rankings over large document collections. 983-990
  
• Le Song, Byron Boots, Sajid M. Siddiqi, Geoffrey J. Gordon, Alexander J. Smola:
  Hilbert Space Embeddings of Hidden Markov Models. 991-998
  
• Sören Sonnenburg, Vojtech Franc:
  COFFIN: A Computational Framework for Linear SVMs. 999-1006
  
• Jonathan Sorg, Satinder P. Singh, Richard L. Lewis:
  Internal Rewards Mitigate Agent Boundedness. 1007-1014
  
• Niranjan Srinivas, Andreas Krause, Sham Kakade, Matthias Seeger:
  Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design. 1015-1022
  
• Zeeshan Syed, Ilan Rubinfeld:
  Unsupervised Risk Stratification in Clinical Datasets: Identifying Patients at Risk of Rare Outcomes. 1023-1030
  
• Istvan Szita, Csaba Szepesvári:
  Model-based reinforcement learning with nearly tight exploration complexity bounds. 1031-1038
  
• Arthur Szlam, Xavier Bresson:
  Total Variation, Cheeger Cuts. 1039-1046
  
• Mingkui Tan, Li Wang, Ivor W. Tsang:
  Learning Sparse SVM for Feature Selection on Very High Dimensional Datasets. 1047-1054
  
• Yichuan Tang, Chris Eliasmith:
  Deep networks for robust visual recognition. 1055-1062
  
• Mamadou Thiao, Pham Dinh Tao, Le Thi Hoai An:
  A DC Programming Approach for Sparse Eigenvalue Problem. 1063-1070
  
• Christophe Thiery, Bruno Scherrer:
  Least-Squares Policy Iteration: Bias-Variance Trade-off in Control Problems. 1071-1078
  
• Daniel Ting, Ling Huang, Michael I. Jordan:
  An Analysis of the Convergence of Graph Laplacians. 1079-1086
  
• Ryota Tomioka, Taiji Suzuki, Masashi Sugiyama, Hisashi Kashima:
  A Fast Augmented Lagrangian Algorithm for Learning Low-Rank Matrices. 1087-1094
  
• Han-Hsing Tu, Hsuan-Tien Lin:
  One-sided Support Vector Regression for Multiclass Cost-sensitive Classification. 1095-1102
  
• David Vickrey, Cliff Chiung-Yu Lin, Daphne Koller:
  Non-Local Contrastive Objectives. 1103-1110
  
• Julia E. Vogt, Sandhya Prabhakaran, Thomas J. Fuchs, Volker Roth:
  The Translation-invariant Wishart-Dirichlet Process for Clustering Distance Data. 1111-1118
  
• Thomas J. Walsh, Kaushik Subramanian, Michael L. Littman, Carlos Diuk:
  Generalizing Apprenticeship Learning across Hypothesis Classes. 1119-1126
  
• Jun Wang, Sanjiv Kumar, Shih-Fu Chang:
  Sequential Projection Learning for Hashing with Compact Codes. 1127-1134
  
• Wei Wang, Zhi-Hua Zhou:
  A New Analysis of Co-Training. 1135-1142
  
• Zhuang Wang, Koby Crammer, Slobodan Vucetic:
  Multi-Class Pegasos on a Budget. 1143-1150
  
• Sinead Williamson, Chong Wang, Katherine A. Heller, David M. Blei:
  The IBP Compound Dirichlet Process and its Application to Focused Topic Modeling. 1151-1158
  
• Xindong Wu, Kui Yu, Hao Wang, Wei Ding:
  Online Streaming Feature Selection. 1159-1166
  
• Michael Wunder, Michael L. Littman, Monica Babes:
  Classes of Multiagent Q-learning Dynamics with epsilon-greedy Exploration. 1167-1174
  
• Zenglin Xu, Rong Jin, Haiqin Yang, Irwin King, Michael R. Lyu:
  Simple and Efficient Multiple Kernel Learning by Group Lasso. 1175-1182
  
• Feng Yan, Yuan (Alan) Qi:
  Sparse Gaussian Process Regression via L1 Penalization. 1183-1190
  
• Haiqin Yang, Zenglin Xu, Irwin King, Michael R. Lyu:
  Online Learning for Group Lasso. 1191-1198
  
• Tianbao Yang, Rong Jin, Anil K. Jain:
  Learning from Noisy Side Information by Generalized Maximum Entropy Model. 1199-1206
  
• Huizhen Yu:
  Convergence of Least Squares Temporal Difference Methods Under General Conditions. 1207-1214
  
• Kai Yu, Tong Zhang:
  Improved Local Coordinate Coding using Local Tangents. 1215-1222
  
• Yi Zhang, Jeff G. Schneider:
  Projection Penalties: Dimension Reduction without Loss. 1223-1230
  
• Peilin Zhao, Steven C. H. Hoi:
  OTL: A Framework of Online Transfer Learning. 1231-1238
  
• Jun Zhu, Eric P. Xing:
  Conditional Topic Random Fields. 1239-1246
  
• Xiaojin Zhu, Bryan R. Gibson, Kwang-Sung Jun, Timothy T. Rogers, Joseph Harrison, Chuck Kalish:
  Cognitive Models of Test-Item Effects in Human Category Learning. 1247-1254
  
• Brian D. Ziebart, J. Andrew Bagnell, Anind K. Dey:
  Modeling Interaction via the Principle of Maximum Causal Entropy. 1255-1262
  

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