25. ICML 2008: Helsinki, Finland
William W. Cohen, Andrew McCallum, Sam T. Roweis (Eds.): Machine Learning, Proceedings of the Twenty-Fifth International Conference (ICML 2008), Helsinki, Finland, June 5-9, 2008. ACM 2008 ACM International Conference Proceeding Series 307 ISBN 978-1-60558-205-4- Ryan Prescott Adams, Oliver Stegle: Gaussian process product models for nonparametric nonstationarity. 1-8
- Cyril Allauzen, Mehryar Mohri, Ameet Talwalkar: Sequence kernels for predicting protein essentiality. 9-16
- Qi An, Chunping Wang, Ivo Shterev, Eric Wang, Lawrence Carin, David B. Dunson: Hierarchical kernel stick-breaking process for multi-task image analysis. 17-24
- Francis R. Bach: Graph kernels between point clouds. 25-32
- Francis R. Bach: Bolasso: model consistent Lasso estimation through the bootstrap. 33-40
- Charles Bergeron, Jed Zaretzki, Curt M. Breneman, Kristin P. Bennett: Multiple instance ranking. 48-55
- Steffen Bickel, Jasmina Bogojeska, Thomas Lengauer, Tobias Scheffer: Multi-task learning for HIV therapy screening. 56-63
- Michael Biggs, Ali Ghodsi, Stephen A. Vavasis: Nonnegative matrix factorization via rank-one downdate. 64-71
- Michael H. Bowling, Michael Johanson, Neil Burch, Duane Szafron: Strategy evaluation in extensive games with importance sampling. 72-79
- Rich Caruana, Nikolaos Karampatziakis, Ainur Yessenalina: An empirical evaluation of supervised learning in high dimensions. 96-103
- Bryan C. Catanzaro, Narayanan Sundaram, Kurt Keutzer: Fast support vector machine training and classification on graphics processors. 104-111
- Lawrence Cayton: Fast nearest neighbor retrieval for bregman divergences. 112-119
- Hakan Cevikalp, Bill Triggs, Robi Polikar: Nearest hyperdisk methods for high-dimensional classification. 120-127
- David L. Chen, Raymond J. Mooney: Learning to sportscast: a test of grounded language acquisition. 128-135
- Adam Coates, Pieter Abbeel, Andrew Y. Ng: Learning for control from multiple demonstrations. 144-151
- Ronan Collobert, Jason Weston: A unified architecture for natural language processing: deep neural networks with multitask learning. 160-167
- Andrés Corrada-Emmanuel, Howard J. Schultz: Autonomous geometric precision error estimation in low-level computer vision tasks. 168-175
- Corinna Cortes, Mehryar Mohri, Dmitry Pechyony, Ashish Rastogi: Stability of transductive regression algorithms. 176-183
- Koby Crammer, Partha Pratim Talukdar, Fernando Pereira: A rate-distortion one-class model and its applications to clustering. 184-191
- John P. Cunningham, Krishna V. Shenoy, Maneesh Sahani: Fast Gaussian process methods for point process intensity estimation. 192-199
- Krzysztof Dembczynski, Wojciech Kotlowski, Roman Slowinski: Maximum likelihood rule ensembles. 224-231
- Uwe Dick, Peter Haider, Tobias Scheffer: Learning from incomplete data with infinite imputations. 232-239
- Carlos Diuk, Andre Cohen, Michael L. Littman: An object-oriented representation for efficient reinforcement learning. 240-247
- Pinar Donmez, Jaime G. Carbonell: Optimizing estimated loss reduction for active sampling in rank learning. 248-255
- Finale Doshi, Joelle Pineau, Nicholas Roy: Reinforcement learning with limited reinforcement: using Bayes risk for active learning in POMDPs. 256-263
- John C. Duchi, Shai Shalev-Shwartz, Yoram Singer, Tushar Chandra: Efficient projections onto the l1-ball for learning in high dimensions. 272-279
- Murat Dundar, Matthias Wolf, Sarang Lakare, Marcos Salganicoff, Vikas C. Raykar: Polyhedral classifier for target detection: a case study: colorectal cancer. 288-295
- Thomas Finley, Thorsten Joachims: Training structural SVMs when exact inference is intractable. 304-311
- Emily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky: An HDP-HMM for systems with state persistence. 312-319
- Vojtech Franc, Sören Sonnenburg: Optimized cutting plane algorithm for support vector machines. 320-327
- Vojtech Franc, Pavel Laskov, Klaus-Robert Müller: Stopping conditions for exact computation of leave-one-out error in support vector machines. 328-335
- Jordan Frank, Shie Mannor, Doina Precup: Reinforcement learning in the presence of rare events. 336-343
- Gholamreza Haffari, Yang Wang, Shaojun Wang, Greg Mori, Feng Jiao: Boosting with incomplete information. 368-375
- Jihun Ham, Daniel D. Lee: Grassmann discriminant analysis: a unifying view on subspace-based learning. 376-383
- Georg Heigold, Thomas Deselaers, Ralf Schlüter, Hermann Ney: Modified MMI/MPE: a direct evaluation of the margin in speech recognition. 384-391
- Katherine A. Heller, Sinead Williamson, Zoubin Ghahramani: Statistical models for partial membership. 392-399
- Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin, S. Sathiya Keerthi, S. Sundararajan: A dual coordinate descent method for large-scale linear SVM. 408-415
- Tuyen N. Huynh, Raymond J. Mooney: Discriminative structure and parameter learning for Markov logic networks. 416-423
- Aapo Hyvärinen, Shohei Shimizu, Patrik O. Hoyer: Causal modelling combining instantaneous and lagged effects: an identifiable model based on non-Gaussianity. 424-431
- Sham M. Kakade, Shai Shalev-Shwartz, Ambuj Tewari: Efficient bandit algorithms for online multiclass prediction. 440-447
- Michael Karlen, Jason Weston, Ayse Erkan, Ronan Collobert: Large scale manifold transduction. 448-455
- Kristian Kersting, Kurt Driessens: Non-parametric policy gradients: a unified treatment of propositional and relational domains. 456-463
- Alexandre Klementiev, Dan Roth, Kevin Small: Unsupervised rank aggregation with distance-based models. 472-479
- Pushmeet Kohli, Alexander Shekhovtsov, Carsten Rother, Vladimir Kolmogorov, Philip H. S. Torr: On partial optimality in multi-label MRFs. 480-487
- J. Zico Kolter, Adam Coates, Andrew Y. Ng, Yi Gu, Charles DuHadway: Space-indexed dynamic programming: learning to follow trajectories. 488-495
- Ondrej Kuzelka, Filip Zelezný: Fast estimation of first-order clause coverage through randomization and maximum likelihood. 504-511
- Yanyan Lan, Tie-Yan Liu, Tao Qin, Zhiming Ma, Hang Li: Query-level stability and generalization in learning to rank. 512-519
- Niels Landwehr: Modeling interleaved hidden processes. 520-527
- Hugo Larochelle, Yoshua Bengio: Classification using discriminative restricted Boltzmann machines. 536-543
- Alessandro Lazaric, Marcello Restelli, Andrea Bonarini: Transfer of samples in batch reinforcement learning. 544-551
- Lihong Li: A worst-case comparison between temporal difference and residual gradient with linear function approximation. 560-567
- Lihong Li, Michael L. Littman, Thomas J. Walsh: Knows what it knows: a framework for self-aware learning. 568-575
- Zhenguo Li, Jianzhuang Liu, Xiaoou Tang: Pairwise constraint propagation by semidefinite programming for semi-supervised classification. 576-583
- Percy Liang, Michael I. Jordan: An asymptotic analysis of generative, discriminative, and pseudolikelihood estimators. 584-591
- Percy Liang, Hal Daumé III, Dan Klein: Structure compilation: trading structure for features. 592-599
- Nicolas Loeff, David A. Forsyth, Deepak Ramachandran: ManifoldBoost: stagewise function approximation for fully-, semi- and un-supervised learning. 600-607
- Philip M. Long, Rocco A. Servedio: Random classification noise defeats all convex potential boosters. 608-615
- Haiping Lu, Konstantinos N. Plataniotis, Anastasios N. Venetsanopoulos: Uncorrelated multilinear principal component analysis through successive variance maximization. 616-623
- Zhengdong Lu, Todd K. Leen, Yonghong Huang, Deniz Erdogmus: A reproducing kernel Hilbert space framework for pairwise time series distances. 624-631
- André F. T. Martins, Mário A. T. Figueiredo, Pedro M. Q. Aguiar, Noah A. Smith, Eric P. Xing: Nonextensive entropic kernels. 640-647
- Neville Mehta, Soumya Ray, Prasad Tadepalli, Thomas G. Dietterich: Automatic discovery and transfer of MAXQ hierarchies. 648-655
- Raghu Meka, Prateek Jain, Constantine Caramanis, Inderjit S. Dhillon: Rank minimization via online learning. 656-663
- Francisco S. Melo, Sean P. Meyn, M. Isabel Ribeiro: An analysis of reinforcement learning with function approximation. 664-671
- M. Pawan Kumar, Philip H. S. Torr: Efficiently solving convex relaxations for MAP estimation. 680-687
- Shravan Matthur Narayanamurthy, Balaraman Ravindran: On the hardness of finding symmetries in Markov decision processes. 688-695
- Siegfried Nijssen: Bayes optimal classification for decision trees. 696-703
- Sebastian Nowozin, Gökhan H. Bakir: A decoupled approach to exemplar-based unsupervised learning. 704-711
- Deirdre B. O'Brien, Maya R. Gupta, Robert M. Gray: Cost-sensitive multi-class classification from probability estimates. 712-719
- Francesco Orabona, Joseph Keshet, Barbara Caputo: The projectron: a bounded kernel-based Perceptron. 720-727
- Jean-François Paiement, Yves Grandvalet, Samy Bengio, Douglas Eck: A distance model for rhythms. 736-743
- Mark Palatucci, Andrew Carlson: On the chance accuracies of large collections of classifiers. 744-751
- Ronald Parr, Lihong Li, Gavin Taylor, Christopher Painter-Wakefield, Michael L. Littman: An analysis of linear models, linear value-function approximation, and feature selection for reinforcement learning. 752-759
- Kai Puolamäki, Antti Ajanki, Samuel Kaski: Learning to learn implicit queries from gaze patterns. 760-767
- Yuting Qi, Dehong Liu, David B. Dunson, Lawrence Carin: Multi-task compressive sensing with Dirichlet process priors. 768-775
- Novi Quadrianto, Alex J. Smola, Tibério S. Caetano, Quoc V. Le: Estimating labels from label proportions. 776-783
- Filip Radlinski, Robert Kleinberg, Thorsten Joachims: Learning diverse rankings with multi-armed bandits. 784-791
- Marc'Aurelio Ranzato, Martin Szummer: Semi-supervised learning of compact document representations with deep networks. 792-799
- Pradeep D. Ravikumar, Alekh Agarwal, Martin J. Wainwright: Message-passing for graph-structured linear programs: proximal projections, convergence and rounding schemes. 800-807
- Vikas C. Raykar, Balaji Krishnapuram, Jinbo Bi, Murat Dundar, R. Bharat Rao: Bayesian multiple instance learning: automatic feature selection and inductive transfer. 808-815
- Joseph Reisinger, Peter Stone, Risto Miikkulainen: Online kernel selection for Bayesian reinforcement learning. 816-823
- Irina Rish, Genady Grabarnik, Guillermo A. Cecchi, Francisco Pereira, Geoffrey J. Gordon: Closed-form supervised dimensionality reduction with generalized linear models. 832-839
- Saharon Rosset: Bi-level path following for cross validated solution of kernel quantile regression. 840-847
- Volker Roth, Bernd Fischer: The Group-Lasso for generalized linear models: uniqueness of solutions and efficient algorithms. 848-855
- Hichem Sahbi, Jean-Yves Audibert, Jaonary Rabarisoa, Renaud Keriven: Robust matching and recognition using context-dependent kernels. 856-863
- Jun Sakuma, Shigenobu Kobayashi, Rebecca N. Wright: Privacy-preserving reinforcement learning. 864-871
- Ruslan Salakhutdinov, Andriy Mnih: Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. 880-887
- Purnamrita Sarkar, Andrew W. Moore, Amit Prakash: Fast incremental proximity search in large graphs. 896-903
- Michael Schnall-Levin, Leonid Chindelevitch, Bonnie Berger: Inverting the Viterbi algorithm: an abstract framework for structure design. 904-911
- Yevgeny Seldin, Naftali Tishby: Multi-classification by categorical features via clustering. 920-927
- Shai Shalev-Shwartz, Nathan Srebro: SVM optimization: inverse dependence on training set size. 928-935
- Tao Shi, Mikhail Belkin, Bin Yu: Data spectroscopy: learning mixture models using eigenspaces of convolution operators. 936-943
- Kilho Shin, Tetsuji Kuboyama: A generalization of Haussler's convolution kernel: mapping kernel. 944-951
- Suyash Shringarpure, Eric P. Xing: mStruct: a new admixture model for inference of population structure in light of both genetic admixing and allele mutations. 952-959
- Christian D. Sigg, Joachim M. Buhmann: Expectation-maximization for sparse and non-negative PCA. 960-967
- David Silver, Richard S. Sutton, Martin Müller: Sample-based learning and search with permanent and transient memories. 968-975
- Vikas Sindhwani, David S. Rosenberg: An RKHS for multi-view learning and manifold co-regularization. 976-983
- Nataliya Sokolovska, Olivier Cappé, François Yvon: The asymptotics of semi-supervised learning in discriminative probabilistic models. 984-991
- Le Song, Xinhua Zhang, Alex J. Smola, Arthur Gretton, Bernhard Schölkopf: Tailoring density estimation via reproducing kernel moment matching. 992-999
- Daria Sorokina, Rich Caruana, Mirek Riedewald, Daniel Fink: Detecting statistical interactions with additive groves of trees. 1000-1007
- Bharath K. Sriperumbudur, Omer A. Lang, Gert R. G. Lanckriet: Metric embedding for kernel classification rules. 1008-1015
- Jiang Su, Harry Zhang, Charles X. Ling, Stan Matwin: Discriminative parameter learning for Bayesian networks. 1016-1023
- Liang Sun, Shuiwang Ji, Jieping Ye: A least squares formulation for canonical correlation analysis. 1024-1031
- Umar Syed, Michael H. Bowling, Robert E. Schapire: Apprenticeship learning using linear programming. 1032-1039
- Akiko Takeda, Masashi Sugiyama: nu-support vector machine as conditional value-at-risk minimization. 1056-1063
- Tijmen Tieleman: Training restricted Boltzmann machines using approximations to the likelihood gradient. 1064-1071
- Tsuyoshi Ueno, Motoaki Kawanabe, Takeshi Mori, Shin-ichi Maeda, Shin Ishii: A semiparametric statistical approach to model-free policy evaluation. 1072-1079
- Raquel Urtasun, David J. Fleet, Andreas Geiger, Jovan Popovic, Trevor Darrell, Neil D. Lawrence: Topologically-constrained latent variable models. 1080-1087
- Jurgen Van Gael, Yunus Saatci, Yee Whye Teh, Zoubin Ghahramani: Beam sampling for the infinite hidden Markov model. 1088-1095
- Pascal Vincent, Hugo Larochelle, Yoshua Bengio, Pierre-Antoine Manzagol: Extracting and composing robust features with denoising autoencoders. 1096-1103
- Christian Walder, Kwang In Kim, Bernhard Schölkopf: Sparse multiscale gaussian process regression. 1112-1119
- Hua-Yan Wang, Qiang Yang, Hong Qin, Hongbin Zha: Dirichlet component analysis: feature extraction for compositional data. 1128-1135
- Hua-Yan Wang, Qiang Yang, Hongbin Zha: Adaptive p-posterior mixture-model kernels for multiple instance learning. 1136-1143
- Wei Wang, Zhi-Hua Zhou: On multi-view active learning and the combination with semi-supervised learning. 1152-1159
- Kilian Q. Weinberger, Lawrence K. Saul: Fast solvers and efficient implementations for distance metric learning. 1160-1167
- Jason Weston, Frédéric Ratle, Ronan Collobert: Deep learning via semi-supervised embedding. 1168-1175
- David Wingate, Satinder P. Singh: Efficiently learning linear-linear exponential family predictive representations of state. 1176-1183
- Fen Xia, Tie-Yan Liu, Jue Wang, Wensheng Zhang, Hang Li: Listwise approach to learning to rank: theory and algorithm. 1192-1199
- Fusun Yaman, Thomas J. Walsh, Michael L. Littman, Marie desJardins: Democratic approximation of lexicographic preference models. 1200-1207
- Jin Yu, S. V. N. Vishwanathan, Simon Günter, Nicol N. Schraudolph: A quasi-Newton approach to non-smooth convex optimization. 1216-1223
- Kai Zhang, Ivor W. Tsang, James T. Kwok: Improved Nyström low-rank approximation and error analysis. 1232-1239
- Zhenjie Zhang, Bing Tian Dai, Anthony K. H. Tung: Estimating local optimums in EM algorithm over Gaussian mixture model. 1240-1247
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