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- Arik Azran: The rendezvous algorithm: multiclass semi-supervised learning with Markov random walks. 49-56
- Rashmin Babaria, J. Saketha Nath, S. Krishnan, K. R. Sivaramakrishnan, Chiranjib Bhattacharyya, M. Narasimha Murty: Focused crawling with scalable ordinal regression solvers. 57-64
- Sourangshu Bhattacharya, Chiranjib Bhattacharyya, Nagasuma Chandra: Structural alignment based kernels for protein structure classification. 73-80
- Steffen Bickel, Michael Brückner, Tobias Scheffer: Discriminative learning for differing training and test distributions. 81-88
- Antoine Bordes, Léon Bottou, Patrick Gallinari, Jason Weston: Solving multiclass support vector machines with LaRank. 89-96
- Brent Bryan, H. Brendan McMahan, Chad M. Schafer, Jeff G. Schneider: Efficiently computing minimax expected-size confidence regions. 97-104
- Ludwig M. Busse, Peter Orbanz, Joachim M. Buhmann: Cluster analysis of heterogeneous rank data. 113-120
- Bin Cao, Dou Shen, Jian-Tao Sun, Qiang Yang, Zheng Chen: Feature selection in a kernel space. 121-128
- Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, Hang Li: Learning to rank: from pairwise approach to listwise approach. 129-136
- Antoni B. Chan, Nuno Vasconcelos, Gert R. G. Lanckriet: Direct convex relaxations of sparse SVM. 145-153
- Xue-wen Chen, Jong Cheol Jeong: Minimum reference set based feature selection for small sample classifications. 153-160
- Alexandre d'Aspremont, Francis R. Bach, Laurent El Ghaoui: Full regularization path for sparse principal component analysis. 177-184
- Jason V. Davis, Brian Kulis, Prateek Jain, Suvrit Sra, Inderjit S. Dhillon: Information-theoretic metric learning. 209-216
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- Erick Delage, Shie Mannor: Percentile optimization in uncertain Markov decision processes with application to efficient exploration. 225-232
- Laura Dietz, Steffen Bickel, Tobias Scheffer: Unsupervised prediction of citation influences. 233-240
- Piotr Dollár, Vincent Rabaud, Serge J. Belongie: Non-isometric manifold learning: analysis and an algorithm. 241-248
- Miroslav Dudík, David M. Blei, Robert E. Schapire: Hierarchical maximum entropy density estimation. 249-256
- Roberto Esposito, Daniele P. Radicioni: CarpeDiem: an algorithm for the fast evaluation of SSL classifiers. 257-264
- Amir Massoud Farahmand, Csaba Szepesvári, Jean-Yves Audibert: Manifold-adaptive dimension estimation. 265-272
- Samuel Gerber, Tolga Tasdizen, Ross T. Whitaker: Robust non-linear dimensionality reduction using successive 1-dimensional Laplacian Eigenmaps. 281-288
- Pierre Geurts, Louis Wehenkel, Florence d'Alché-Buc: Gradient boosting for kernelized output spaces. 289-296
- Amir Globerson, Terry Koo, Xavier Carreras, Michael Collins: Exponentiated gradient algorithms for log-linear structured prediction. 305-312
- Nizar Grira, Michael E. Houle: Best of both: a hybridized centroid-medoid clustering heuristic. 313-320
- Fan Guo, Steve Hanneke, Wenjie Fu, Eric P. Xing: Recovering temporally rewiring networks: a model-based approach. 321-328
- Rahul Gupta, Ajit A. Diwan, Sunita Sarawagi: Efficient inference with cardinality-based clique potentials. 329-336
- Peter Haider, Ulf Brefeld, Tobias Scheffer: Supervised clustering of streaming data for email batch detection. 345-352
- Steve Hanneke: A bound on the label complexity of agnostic active learning. 353-360
- Steven C. H. Hoi, Rong Jin, Michael R. Lyu: Learning nonparametric kernel matrices from pairwise constraints. 361-368
- Manfred Jaeger: Parameter learning for relational Bayesian networks. 369-376
- Jeffrey Johns, Sridhar Mahadevan: Constructing basis functions from directed graphs for value function approximation. 385-392
- Kristian Kersting, Christian Plagemann, Patrick Pfaff, Wolfram Burgard: Most likely heteroscedastic Gaussian process regression. 393-400
- Kye-Hyeon Kim, Seungjin Choi: Neighbor search with global geometry: a minimax message passing algorithm. 401-408
- Nicole Krämer, Mikio L. Braun: Kernelizing PLS, degrees of freedom, and efficient model selection. 441-448
- Andreas Krause, Carlos Guestrin: Nonmyopic active learning of Gaussian processes: an exploration-exploitation approach. 449-456
- Dmitry Kropotov, Dmitry Vetrov: On one method of non-diagonal regularization in sparse Bayesian learning. 457-464
- Hugo Larochelle, Dumitru Erhan, Aaron C. Courville, James Bergstra, Yoshua Bengio: An empirical evaluation of deep architectures on problems with many factors of variation. 473-480
- Su-In Lee, Vassil Chatalbashev, David Vickrey, Daphne Koller: Learning a meta-level prior for feature relevance from multiple related tasks. 489-496
- Jure Leskovec, Christos Faloutsos: Scalable modeling of real graphs using Kronecker multiplication. 497-504
- Chris H. Q. Ding, Tao Li: Adaptive dimension reduction using discriminant analysis and K-means clustering. 521-528
- Xin Li, William Kwok-Wai Cheung, Jiming Liu, Zhili Wu: A novel orthogonal NMF-based belief compression for POMDPs. 537-544
- Percy Liang, Michael I. Jordan, Benjamin Taskar: A permutation-augmented sampler for DP mixture models. 545-552
- Xuejun Liao, Hui Li, Lawrence Carin: Quadratically gated mixture of experts for incomplete data classification. 553-560
- Chih-Jen Lin, Ruby C. Weng, S. Sathiya Keerthi: Trust region Newton methods for large-scale logistic regression. 561-568
- Bo Long, Zhongfei (Mark) Zhang, Xiaoyun Wu, Philip S. Yu: Relational clustering by symmetric convex coding. 569-576
- Yong Ma, Shihong Lao, Erina Takikawa, Masato Kawade: Discriminant analysis in correlation similarity measure space. 577-584
- Sridhar Mahadevan: Adaptive mesh compression in 3D computer graphics using multiscale manifold learning. 585-592
- Gideon S. Mann, Andrew McCallum: Simple, robust, scalable semi-supervised learning via expectation regularization. 593-600
- Bhaskara Marthi: Automatic shaping and decomposition of reward functions. 601-608
- Graham McNeill, Sethu Vijayakumar: Linear and nonlinear generative probabilistic class models for shape contours. 617-624
- David M. Mimno, Wei Li, Andrew McCallum: Mixtures of hierarchical topics with Pachinko allocation. 633-640
- Andriy Mnih, Geoffrey E. Hinton: Three new graphical models for statistical language modelling. 641-648
- Alessandro Moschitti, Fabio Massimo Zanzotto: Fast and effective kernels for relational learning from texts. 649-656
- Sofia Mosci, Lorenzo Rosasco, Alessandro Verri: Dimensionality reduction and generalization. 657-664
- Markos Mylonakis, Khalil Sima'an, Rebecca Hwa: Unsupervised estimation for noisy-channel models. 665-672
- Blaine Nelson, Ira Cohen: Revisiting probabilistic models for clustering with pair-wise constraints. 673-680
- Kai Ni, Lawrence Carin, David B. Dunson: Multi-task learning for sequential data via iHMMs and the nested Dirichlet process. 689-696
- Jens Nilsson, Fei Sha, Michael I. Jordan: Regression on manifolds using kernel dimension reduction. 697-704
- Sarah Osentoski, Sridhar Mahadevan: Learning state-action basis functions for hierarchical MDPs. 705-712
- A. P. Yogananda, M. Narasimha Murty, Lakshmi Gopal: A fast linear separability test by projection of positive points on subspaces. 713-720
- Sandeep Pandey, Deepayan Chakrabarti, Deepak Agarwal: Multi-armed bandit problems with dependent arms. 721-728
- Charles Parker, Alan Fern, Prasad Tadepalli: Learning for efficient retrieval of structured data with noisy queries. 729-736
- Ronald Parr, Christopher Painter-Wakefield, Lihong Li, Michael L. Littman: Analyzing feature generation for value-function approximation. 737-744
- Jan Peters, Stefan Schaal: Reinforcement learning by reward-weighted regression for operational space control. 745-750
- Chee Wee Phua, Robert Fitch: Tracking value function dynamics to improve reinforcement learning with piecewise linear function approximation. 751-758
- Rajat Raina, Alexis Battle, Honglak Lee, Benjamin Packer, Andrew Y. Ng: Self-taught learning: transfer learning from unlabeled data. 759-766
- Alexander Rakhlin, Jacob Abernethy, Peter L. Bartlett: Online discovery of similarity mappings. 767-774
- Alain Rakotomamonjy, Francis Bach, Stéphane Canu, Yves Grandvalet: More efficiency in multiple kernel learning. 775-782
- Matthew J. Rattigan, Marc E. Maier, David Jensen: Graph clustering with network structure indices. 783-790
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- Shai Shalev-Shwartz, Yoram Singer, Nathan Srebro: Pegasos: Primal Estimated sub-GrAdient SOlver for SVM. 807-814
- Le Song, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt: A dependence maximization view of clustering. 815-822
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- Gabriel Wachman, Roni Khardon: Learning from interpretations: a rooted kernel for ordered hypergraphs. 943-950
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- Hua-Yan Wang, Hongbin Zha, Hong Qin: Dirichlet aggregation: unsupervised learning towards an optimal metric for proportional data. 959-966
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- Manfred K. Warmuth: Winnowing subspaces. 999-1006
- Tomás Werner: What is decreased by the max-sum arc consistency algorithm? 1007-1014
- Aaron Wilson, Alan Fern, Soumya Ray, Prasad Tadepalli: Multi-task reinforcement learning: a hierarchical Bayesian approach. 1015-1022
- Adam Woznica, Alexandros Kalousis, Melanie Hilario: Learning to combine distances for complex representations. 1031-1038
- Xiang Xuan, Kevin P. Murphy: Modeling changing dependency structure in multivariate time series. 1055-1062
- Ya Xue, David B. Dunson, Lawrence Carin: The matrix stick-breaking process for flexible multi-task learning. 1063-1070
- Takehisa Yairi: Map building without localization by dimensionality reduction techniques. 1071-1078
- Keisuke Yamazaki, Motoaki Kawanabe, Sumio Watanabe, Masashi Sugiyama, Klaus-Robert Müller: Asymptotic Bayesian generalization error when training and test distributions are different. 1079-1086
- Jieping Ye: Least squares linear discriminant analysis. 1087-1093
- Jieping Ye, Jianhui Chen, Shuiwang Ji: Discriminant kernel and regularization parameter learning via semidefinite programming. 1095-1102
- Jian Zhang, Rong Yan: On the value of pairwise constraints in classification and consistency. 1111-1118
- Kun Zhang, Laiwan Chan: Nonlinear independent component analysis with minimal nonlinear distortion. 1127-1134
- Wei Zhang, Xiangyang Xue, Zichen Sun, Yue-Fei Guo, Hong Lu: Optimal dimensionality of metric space for classification. 1135-1142
- Xinhua Zhang, Douglas Aberdeen, S. V. N. Vishwanathan: Conditional random fields for multi-agent reinforcement learning. 1143-1150
- Zheng Zhao, Huan Liu: Spectral feature selection for supervised and unsupervised learning. 1151-1157
- Dengyong Zhou, Christopher J. C. Burges: Spectral clustering and transductive learning with multiple views. 1159-1166
- Zhi-Hua Zhou, Jun-Ming Xu: On the relation between multi-instance learning and semi-supervised learning. 1167-1174
- Jun Zhu, Zaiqing Nie, Bo Zhang, Ji-Rong Wen: Dynamic hierarchical Markov random fields and their application to web data extraction. 1175-1182
- Alexander Zien, Ulf Brefeld, Tobias Scheffer: Transductive support vector machines for structured variables. 1183-1190
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