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ORCID23rd ICML 2006: Pittsburgh, Pennsylvania, USA
- William W. Cohen, Andrew W. Moore:
Machine Learning, Proceedings of the Twenty-Third International Conference (ICML 2006), Pittsburgh, Pennsylvania, USA, June 25-29, 2006. ACM International Conference Proceeding Series 148, ACM 2006, ISBN 1-59593-383-2 - 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 J. 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 M. 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 H. 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 Dimitri 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, Alexander 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 D. 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 J. McAuley, Tibério S. Caetano, Alexander 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 J. 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 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 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 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 Bezerra de Andrade e 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 Singh:
Kernel Predictive Linear Gaussian models for nonlinear stochastic dynamical systems. 1017-1024 - Britton Wolfe, Satinder 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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