27. ICML 2010: Haifa, Israel
Johannes Fürnkranz, Thorsten Joachims (Eds.): Proceedings of the 27th International Conference on Machine Learning (ICML-10), June 21-24, 2010, Haifa, Israel. Omnipress 2010 ISBN 978-1-60558-907-7- 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
- 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
- Nader H. Bshouty, Philip M. Long: Finding Planted Partitions in Nearly Linear Time using Arrested Spectral Clustering. 135-142
- 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
- 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
- Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh: Generalization Bounds for Learning Kernels. 247-254
- 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 Lèbre, 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
- Lev Faivishevsky, Jacob Goldberger: Nonparametric Information Theoretic Clustering Algorithm. 351-358
- 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
- Noam Goldberg, Jonathan Eckstein: Boosting Classifiers with Tightened L0-Relaxation Penalties. 383-390
- Alexander Grubb, J. Andrew Bagnell: Boosted Backpropagation Learning for Training Deep Modular Networks. 407-414
- Bharath Hariharan, Lihi Zelnik-Manor, S. V. N. Vishwanathan, Manik Varma: Large Scale Max-Margin Multi-Label Classification with Priors. 423-430
- Matthew D. Hoffman, David M. Blei, Perry R. Cook: Bayesian Nonparametric Matrix Factorization for Recorded Music. 439-446
- Jonathan Huang, Carlos Guestrin: Learning Hierarchical Riffle Independent Groupings from Rankings. 455-462
- 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
- Seyoung Kim, Eric P. Xing: Tree-Guided Group Lasso for Multi-Task Regression with Structured Sparsity. 543-550
- Mladen Kolar, Ankur P. Parikh, Eric P. Xing: On Sparse Nonparametric Conditional Covariance Selection. 559-566
- 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, Rémi Munos: Analysis of a Classification-based Policy Iteration Algorithm. 607-614
- 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
- Guangcan Liu, Zhouchen Lin, Yong Yu: Robust Subspace Segmentation by Low-Rank Representation. 663-670
- 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
- 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
- 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
- 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
- Ruslan Salakhutdinov: Learning Deep Boltzmann Machines using Adaptive MCMC. 943-950
- 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
- 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
- Mingkui Tan, Li Wang, Ivor W. Tsang: Learning Sparse SVM for Feature Selection on Very High Dimensional Datasets. 1047-1054
- 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
- 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
- Sinead Williamson, Chong Wang, Katherine A. Heller, David M. Blei: The IBP Compound Dirichlet Process and its Application to Focused Topic Modeling. 1151-1158
- 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
- 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
- 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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