23. ICML 2006: Pittsburgh, Pennsylvania, USA

• Pieter Abbeel, Morgan Quigley, Andrew Y. Ng:
  Using inaccurate models in reinforcement learning. 1-8
  
• Amit Agarwal, Elad Hazan, Satyen Kale, Robert E. Schapire:
  Algorithms for portfolio management based on the Newton method. 9-16
  
• Sameer Agarwal, Kristin Branson, Serge Belongie:
  Higher order learning with graphs. 17-24
  
• Shivani Agarwal:
  Ranking on graph data. 25-32
  
• Cédric Archambeau, Nicolas Delannay, Michel Verleysen:
  Robust probabilistic projections. 33-40
  
• Andreas Argyriou, Raphael Hauser, Charles A. Micchelli, Massimiliano Pontil:
  A DC-programming algorithm for kernel selection. 41-48
  
• Nima Asgharbeygi, David J. Stracuzzi, Pat Langley:
  Relational temporal difference learning. 49-56
  
• Arik Azran, Zoubin Ghahramani:
  A new approach to data driven clustering. 57-64
  
• Maria-Florina Balcan, Alina Beygelzimer, John Langford:
  Agnostic active learning. 65-72
  
• Maria-Florina Balcan, Avrim Blum:
  On a theory of learning with similarity functions. 73-80
  
• Arindam Banerjee:
  On Bayesian bounds. 81-88
  
• Onureena Banerjee, Laurent El Ghaoui, Alexandre d'Aspremont, Georges Natsoulis:
  Convex optimization techniques for fitting sparse Gaussian graphical models. 89-96
  
• Alina Beygelzimer, Sham Kakade, John Langford:
  Cover trees for nearest neighbor. 97-104
  
• Ivona Bezáková, Adam Kalai, Rahul Santhanam:
  Graph model selection using maximum likelihood. 105-112
  
• David M. Blei, John D. Lafferty:
  Dynamic topic models. 113-120
  
• Edwin V. Bonilla, Christopher K. I. Williams, Felix V. Agakov, John Cavazos, John Thomson, Michael F. P. O'Boyle:
  Predictive search distributions. 121-128
  
• Michael H. Bowling, Peter McCracken, Michael James, James Neufeld, Dana F. Wilkinson:
  Learning predictive state representations using non-blind policies. 129-136
  
• Ulf Brefeld, Thomas Gärtner, Tobias Scheffer, Stefan Wrobel:
  Efficient co-regularised least squares regression. 137-144
  
• Ulf Brefeld, Tobias Scheffer:
  Semi-supervised learning for structured output variables. 145-152
  
• Miguel Á. Carreira-Perpiñán:
  Fast nonparametric clustering with Gaussian blurring mean-shift. 153-160
  
• Rich Caruana, Alexandru Niculescu-Mizil:
  An empirical comparison of supervised learning algorithms. 161-168
  
• Lawrence Cayton, Sanjoy Dasgupta:
  Robust Euclidean embedding. 169-176
  
• Nicolò Cesa-Bianchi, Claudio Gentile, Luca Zaniboni:
  Hierarchical classification: combining Bayes with SVM. 177-184
  
• Olivier Chapelle, Mingmin Chi, Alexander Zien:
  A continuation method for semi-supervised SVMs. 185-192
  
• Pak-Ming Cheung, James T. Kwok:
  A regularization framework for multiple-instance learning. 193-200
  
• Ronan Collobert, Fabian H. Sinz, Jason Weston, Léon Bottou:
  Trading convexity for scalability. 201-208
  
• Vincent Conitzer, Nikesh Garera:
  Learning algorithms for online principal-agent problems (and selling goods online). 209-216
  
• Bruno Castro da Silva, Eduardo W. Basso, Ana L. C. Bazzan, Paulo Martins Engel:
  Dealing with non-stationary environments using context detection. 217-224
  
• Juan Dai, Shuicheng Yan, Xiaoou Tang, James T. Kwok:
  Locally adaptive classification piloted by uncertainty. 225-232
  
• Jesse Davis, Mark Goadrich:
  The relationship between Precision-Recall and ROC curves. 233-240
  
• Fernando De la Torre, Takeo Kanade:
  Discriminative cluster analysis. 241-248
  
• Dennis DeCoste:
  Collaborative prediction using ensembles of Maximum Margin Matrix Factorizations. 249-256
  
• Thomas Degris, Olivier Sigaud, Pierre-Henri Wuillemin:
  Learning the structure of Factored Markov Decision Processes in reinforcement learning problems. 257-264
  
• François Denis, Christophe Nicolas Magnan, Liva Ralaivola:
  Efficient learning of Naive Bayes classifiers under class-conditional classification noise. 265-272
  
• Marie desJardins, Eric Eaton, Kiri Wagstaff:
  Learning user preferences for sets of objects. 273-280
  
• Chris H. Q. Ding, Ding Zhou, Xiaofeng He, Hongyuan Zha:
  R₁-PCA: rotational invariant L₁-norm principal component analysis for robust subspace factorization. 281-288
  
• Charles Elkan:
  Clustering documents with an exponential-family approximation of the Dirichlet compound multinomial distribution. 289-296
  
• Barbara E. Engelhardt, Michael I. Jordan, Steven E. Brenner:
  A graphical model for predicting protein molecular function. 297-304
  
• Arkady Epshteyn, Gerald DeJong:
  Qualitative reinforcement learning. 305-312
  
• Michael Fink, Shai Shalev-Shwartz, Yoram Singer, Shimon Ullman:
  Online multiclass learning by interclass hypothesis sharing. 313-320
  
• Jochen Garcke:
  Regression with the optimised combination technique. 321-328
  
• Yang Ge, Wenxin Jiang:
  A note on mixtures of experts for multiclass responses: approximation rate and Consistent Bayesian Inference. 329-335
  
• Peter V. Gehler, Alex Holub, Max Welling:
  The rate adapting poisson model for information retrieval and object recognition. 337-344
  
• Pierre Geurts, Louis Wehenkel, Florence d'Alché-Buc:
  Kernelizing the output of tree-based methods. 345-352
  
• Amir Globerson, Sam T. Roweis:
  Nightmare at test time: robust learning by feature deletion. 353-360
  
• Dilan Görür, Frank Jäkel, Carl Edward Rasmussen:
  A choice model with infinitely many latent features. 361-368
  
• Alex Graves, Santiago Fernández, Faustino J. Gomez, Jürgen Schmidhuber:
  Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. 369-376
  
• Derek Greene, Padraig Cunningham:
  Practical solutions to the problem of diagonal dominance in kernel document clustering. 377-384
  
• Patrick Haffner:
  Fast transpose methods for kernel learning on sparse data. 385-392
  
• Steve Hanneke:
  An analysis of graph cut size for transductive learning. 393-399
  
• Tomer Hertz, Aharon Bar-Hillel, Daphna Weinshall:
  Learning a kernel function for classification with small training samples. 401-408
  
• Michael P. Holmes, Charles Lee Isbell Jr.:
  Looping suffix tree-based inference of partially observable hidden state. 409-416
  
• Steven C. H. Hoi, Rong Jin, Jianke Zhu, Michael R. Lyu:
  Batch mode active learning and its application to medical image classification. 417-424
  
• Tzu-Kuo Huang, Chih-Jen Lin, Ruby C. Weng:
  Ranking individuals by group comparisons. 425-432
  
• Rebecca A. Hutchinson, Tom M. Mitchell, Indrayana Rustandi:
  Hidden process models. 433-440
  
• Brendan Juba:
  Estimating relatedness via data compression. 441-448
  
• Philipp W. Keller, Shie Mannor, Doina Precup:
  Automatic basis function construction for approximate dynamic programming and reinforcement learning. 449-456
  
• Wolf Kienzle, Kumar Chellapilla:
  Personalized handwriting recognition via biased regularization. 457-464
  
• Seung-Jean Kim, Alessandro Magnani, Stephen P. Boyd:
  Optimal kernel selection in Kernel Fisher discriminant analysis. 465-472
  
• Seung-Jean Kim, Alessandro Magnani, Sikandar Samar, Stephen P. Boyd, Johan Lim:
  Pareto optimal linear classification. 473-480
  
• Mike Klaas, Mark Briers, Nando de Freitas, Arnaud Doucet, Simon Maskell, Dustin Lang:
  Fast particle smoothing: if I had a million particles. 481-488
  
• George Konidaris, Andrew G. Barto:
  Autonomous shaping: knowledge transfer in reinforcement learning. 489-496
  
• Andreas Krause, Jure Leskovec, Carlos Guestrin:
  Data association for topic intensity tracking. 497-504
  
• Brian Kulis, Mátyás A. Sustik, Inderjit S. Dhillon:
  Learning low-rank kernel matrices. 505-512
  
• Neil D. Lawrence, Joaquin Quiñonero Candela:
  Local distance preservation in the GP-LVM through back constraints. 513-520
  
• Quoc V. Le, Alex J. Smola, Thomas Gärtner:
  Simpler knowledge-based support vector machines. 521-528
  
• Chi-Hoon Lee, Russell Greiner, Shaojun Wang:
  Using query-specific variance estimates to combine Bayesian classifiers. 529-536
  
• Alain Lehmann, John Shawe-Taylor:
  A probabilistic model for text kernels. 537-544
  
• Marius Leordeanu, Martial Hebert:
  Efficient MAP approximation for dense energy functions. 545-552
  
• Darrin P. Lewis, Tony Jebara, William Stafford Noble:
  Nonstationary kernel combination. 553-560
  
• Hui Li, Xuejun Liao, Lawrence Carin:
  Region-based value iteration for partially observable Markov decision processes. 561-568
  
• Ling Li:
  Multiclass boosting with repartitioning. 569-576
  
• Wei Li, Andrew McCallum:
  Pachinko allocation: DAG-structured mixture models of topic correlations. 577-584
  
• Bo Long, Zhongfei (Mark) Zhang, Xiaoyun Wu, Philip S. Yu:
  Spectral clustering for multi-type relational data. 585-592
  
• Le Lu, René Vidal:
  Combined central and subspace clustering for computer vision applications. 593-600
  
• Mauro Maggioni, Sridhar Mahadevan:
  Fast direct policy evaluation using multiscale analysis of Markov diffusion processes. 601-608
  
• Gonzalo Martínez-Muñoz, Alberto Suárez:
  Pruning in ordered bagging ensembles. 609-616
  
• Julian John McAuley, Tibério S. Caetano, Alex J. Smola, Matthias O. Franz:
  Learning high-order MRF priors of color images. 617-624
  
• Marina Meila:
  The uniqueness of a good optimum for K-means. 625-632
  
• Roland Memisevic:
  Kernel information embeddings. 633-640
  
• Baback Moghaddam, Yair Weiss, Shai Avidan:
  Generalized spectral bounds for sparse LDA. 641-648
  
• Moni Naor, Guy N. Rothblum:
  Learning to impersonate. 649-656
  
• Mukund Narasimhan, Paul A. Viola, Michael Shilman:
  Online decoding of Markov models under latency constraints. 657-664
  
• Negin Nejati, Pat Langley, Tolga Könik:
  Learning hierarchical task networks by observation. 665-672
  
• Yuriy Nevmyvaka, Yi Feng, Michael Kearns:
  Reinforcement learning for optimized trade execution. 673-680
  
• Navneet Panda, Edward Y. Chang, Gang Wu:
  Concept boundary detection for speeding up SVMs. 681-688
  
• Francisco Pereira, Geoffrey J. Gordon:
  The support vector decomposition machine. 689-696
  
• Pascal Poupart, Nikos A. Vlassis, Jesse Hoey, Kevin Regan:
  An analytic solution to discrete Bayesian reinforcement learning. 697-704
  
• Rouhollah Rahmani, Sally A. Goldman:
  MISSL: multiple-instance semi-supervised learning. 705-712
  
• Rajat Raina, Andrew Y. Ng, Daphne Koller:
  Constructing informative priors using transfer learning. 713-720
  
• Liva Ralaivola, François Denis, Christophe Nicolas Magnan:
  CN = CPCN. 721-728
  
• Nathan D. Ratliff, J. Andrew Bagnell, Martin Zinkevich:
  Maximum margin planning. 729-736
  
• Pradeep D. Ravikumar, John D. Lafferty:
  Quadratic programming relaxations for metric labeling and Markov random field MAP estimation. 737-744
  
• Jean-Michel Renders, Éric Gaussier, Cyril Goutte, François Pacull, Gabriela Csurka:
  Categorization in multiple category systems. 745-752
  
• Lev Reyzin, Robert E. Schapire:
  How boosting the margin can also boost classifier complexity. 753-760
  
• David A. Ross, Simon Osindero, Richard S. Zemel:
  Combining discriminative features to infer complex trajectories. 761-768
  
• Josep Roure, Andrew W. Moore:
  Sequential update of ADtrees. 769-776
  
• Matthew R. Rudary, Satinder P. Singh:
  Predictive linear-Gaussian models of controlled stochastic dynamical systems. 777-784
  
• Ulrich Rückert, Stefan Kramer:
  A statistical approach to rule learning. 785-792
  
• Sunita Sarawagi:
  Efficient inference on sequence segmentation models. 793-800
  
• Prithviraj Sen, Lise Getoor:
  Cost-sensitive learning with conditional Markov networks. 801-808
  
• Victor S. Sheng, Charles X. Ling:
  Feature value acquisition in testing: a sequential batch test algorithm. 809-816
  
• Pannagadatta K. Shivaswamy, Tony Jebara:
  Permutation invariant SVMs. 817-824
  
• Ricardo Silva, Richard Scheines:
  Bayesian learning of measurement and structural models. 825-832
  
• Özgür Simsek, Andrew G. Barto:
  An intrinsic reward mechanism for efficient exploration. 833-840
  
• Vikas Sindhwani, S. Sathiya Keerthi, Olivier Chapelle:
  Deterministic annealing for semi-supervised kernel machines. 841-848
  
• Surendra K. Singhi, Huan Liu:
  Feature subset selection bias for classification learning. 849-856
  
• Le Song, Julien Epps:
  Classifying EEG for brain-computer interfaces: learning optimal filters for dynamical system features. 857-864
  
• Nathan Srebro, Gregory Shakhnarovich, Sam T. Roweis:
  An investigation of computational and informational limits in Gaussian mixture clustering. 865-872
  
• David H. Stern, Ralf Herbrich, Thore Graepel:
  Bayesian pattern ranking for move prediction in the game of Go. 873-880
  
• Alexander L. Strehl, Lihong Li, Eric Wiewiora, John Langford, Michael L. Littman:
  PAC model-free reinforcement learning. 881-888
  
• Alexander L. Strehl, Chris Mesterharm, Michael L. Littman, Haym Hirsh:
  Experience-efficient learning in associative bandit problems. 889-896
  
• Jiang Su, Harry Zhang:
  Full Bayesian network classifiers. 897-904
  
• Masashi Sugiyama:
  Local Fisher discriminant analysis for supervised dimensionality reduction. 905-912
  
• Yijun Sun, Jian Li:
  Iterative RELIEF for feature weighting. 913-920
  
• Benyang Tang, Dominic Mazzoni:
  Multiclass reduced-set support vector machines. 921-928
  
• Choon Hui Teo, S. V. N. Vishwanathan:
  Fast and space efficient string kernels using suffix arrays. 929-936
  
• Jo-Anne Ting, Aaron D'Souza, Stefan Schaal:
  Bayesian regression with input noise for high dimensional data. 937-944
  
• Marc Toussaint, Amos J. Storkey:
  Probabilistic inference for solving discrete and continuous state Markov Decision Processes. 945-952
  
• Koji Tsuda, Taku Kudo:
  Clustering graphs by weighted substructure mining. 953-960
  
• Sriharsha Veeramachaneni, Emanuele Olivetti, Paolo Avesani:
  Active sampling for detecting irrelevant features. 961-968
  
• S. V. N. Vishwanathan, Nicol N. Schraudolph, Mark W. Schmidt, Kevin P. Murphy:
  Accelerated training of conditional random fields with stochastic gradient methods. 969-976
  
• Hanna M. Wallach:
  Topic modeling: beyond bag-of-words. 977-984
  
• Fei Wang, Changshui Zhang:
  Label propagation through linear neighborhoods. 985-992
  
• Gang Wang, Dit-Yan Yeung, Frederick H. Lochovsky:
  Two-dimensional solution path for support vector regression. 993-1000
  
• Manfred K. Warmuth, Jun Liao, Gunnar Rätsch:
  Totally corrective boosting algorithms that maximize the margin. 1001-1008
  
• Jason Weston, Ronan Collobert, Fabian H. Sinz, Léon Bottou, Vladimir Vapnik:
  Inference with the Universum. 1009-1016
  
• David Wingate, Satinder P. Singh:
  Kernel Predictive Linear Gaussian models for nonlinear stochastic dynamical systems. 1017-1024
  
• Britton Wolfe, Satinder P. Singh:
  Predictive state representations with options. 1025-1032
  
• Xiaopeng Xi, Eamonn J. Keogh, Christian R. Shelton, Li Wei, Chotirat Ann Ratanamahatana:
  Fast time series classification using numerosity reduction. 1033-1040
  
• Lin Xiao, Jun Sun, Stephen P. Boyd:
  A duality view of spectral methods for dimensionality reduction. 1041-1048
  
• Eric P. Xing, Kyung-Ah Sohn, Michael I. Jordan, Yee Whye Teh:
  Bayesian multi-population haplotype inference via a hierarchical dirichlet process mixture. 1049-1056
  
• Linli Xu, Dana F. Wilkinson, Finnegan Southey, Dale Schuurmans:
  Discriminative unsupervised learning of structured predictors. 1057-1064
  
• Xin Yang, Haoying Fu, Hongyuan Zha, Jesse L. Barlow:
  Semi-supervised nonlinear dimensionality reduction. 1065-1072
  
• Jieping Ye, Tao Xiong:
  Null space versus orthogonal linear discriminant analysis. 1073-1080
  
• Kai Yu, Jinbo Bi, Volker Tresp:
  Active learning via transductive experimental design. 1081-1088
  
• Shipeng Yu, Kai Yu, Volker Tresp, Hans-Peter Kriegel:
  Collaborative ordinal regression. 1089-1096
  
• Kai Zhang, James T. Kwok:
  Block-quantized kernel matrix for fast spectral embedding. 1097-1104
  
• Alice X. Zheng, Michael I. Jordan, Ben Liblit, Mayur Naik, Alex Aiken:
  Statistical debugging: simultaneous identification of multiple bugs. 1105-1112
  
• Fei Zheng, Geoffrey I. Webb:
  Efficient lazy elimination for averaged one-dependence estimators. 1113-1120
  

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