25. ICML 2008: Helsinki, Finland

• 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
  
• Leon Barrett, Srini Narayanan:
  Learning all optimal policies with multiple criteria. 41-47
  
• 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
  
• Brent Bryan, Jeff G. Schneider:
  Actively learning level-sets of composite functions. 80-87
  
• Francois Caron, Arnaud Doucet:
  Sparse Bayesian nonparametric regression. 88-95
  
• 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
  
• Jianhui Chen, Jieping Ye:
  Training SVM with indefinite kernels. 136-143
  
• Adam Coates, Pieter Abbeel, Andrew Y. Ng:
  Learning for control from multiple demonstrations. 144-151
  
• Tom Coleman, James Saunderson, Anthony Wirth:
  Spectral clustering with inconsistent advice. 152-159
  
• 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
  
• Wenyuan Dai, Qiang Yang, Gui-Rong Xue, Yong Yu:
  Self-taught clustering. 200-207
  
• Sanjoy Dasgupta, Daniel Hsu:
  Hierarchical sampling for active learning. 208-215
  
• Ofer Dekel, Ohad Shamir:
  Learning to classify with missing and corrupted features. 216-223
  
• 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
  
• Mark Dredze, Koby Crammer, Fernando Pereira:
  Confidence-weighted linear classification. 264-271
  
• John C. Duchi, Shai Shalev-Shwartz, Yoram Singer, Tushar Chandra:
  Efficient projections onto the l₁-ball for learning in high dimensions. 272-279
  
• Charles Dugas, David Gadoury:
  Pointwise exact bootstrap distributions of cost curves. 280-287
  
• Murat Dundar, Matthias Wolf, Sarang Lakare, Marcos Salganicoff, Vikas C. Raykar:
  Polyhedral classifier for target detection: a case study: colorectal cancer. 288-295
  
• Arkady Epshteyn, Adam Vogel, Gerald DeJong:
  Active reinforcement learning. 296-303
  
• 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
  
• Ryan Gomes, Max Welling, Pietro Perona:
  Memory bounded inference in topic models. 344-351
  
• Mehmet Gönen, Ethem Alpaydin:
  Localized multiple kernel learning. 352-359
  
• Geoffrey J. Gordon, Amy Greenwald, Casey Marks:
  No-regret learning in convex games. 360-367
  
• 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
  
• Steven C. H. Hoi, Rong Jin:
  Active kernel learning. 400-407
  
• 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
  
• Sergey Kirshner, Barnabás Póczos:
  ICA and ISA using Schweizer-Wolff measure of dependence. 464-471
  
• 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
  
• Risi Kondor, Karsten M. Borgwardt:
  The skew spectrum of graphs. 496-503
  
• 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
  
• John Langford, Alexander L. Strehl, Jennifer Wortman:
  Exploration scavenging. 528-535
  
• 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
  
• Guy Lebanon, Yang Zhao:
  Local likelihood modeling of temporal text streams. 552-559
  
• 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
  
• Takaki Makino, Toshihisa Takagi:
  On-line discovery of temporal-difference networks. 632-639
  
• 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
  
• Volodymyr Mnih, Csaba Szepesvári, Jean-Yves Audibert:
  Empirical Bernstein stopping. 672-679
  
• 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
  
• Hua Ouyang, Alexander G. Gray:
  Learning dissimilarities by ranking: from SDP to QP. 728-735
  
• 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
  
• Lu Ren, David B. Dunson, Lawrence Carin:
  The dynamic hierarchical Dirichlet process. 824-831
  
• 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, Iain Murray:
  On the quantitative analysis of deep belief networks. 872-879
  
• Ruslan Salakhutdinov, Andriy Mnih:
  Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. 880-887
  
• Sunita Sarawagi, Rahul Gupta:
  Accurate max-margin training for structured output spaces. 888-895
  
• 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
  
• Matthias W. Seeger, Hannes Nickisch:
  Compressed sensing and Bayesian experimental design. 912-919
  
• 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
  
• Marie Szafranski, Yves Grandvalet, Alain Rakotomamonjy:
  Composite kernel learning. 1040-1047
  
• Istvan Szita, András Lörincz:
  The many faces of optimism: a unifying approach. 1048-1055
  
• 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
  
• Vladimir Vovk, Fedor Zhdanov:
  Prediction with expert advice for the Brier game. 1104-1111
  
• Christian Walder, Kwang In Kim, Bernhard Schölkopf:
  Sparse multiscale gaussian process regression. 1112-1119
  
• Chang Wang, Sridhar Mahadevan:
  Manifold alignment using Procrustes analysis. 1120-1127
  
• 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
  
• Jun Wang, Tony Jebara, Shih-Fu Chang:
  Graph transduction via alternating minimization. 1144-1151
  
• 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
  
• Jason Wolfe, Aria Haghighi, Dan Klein:
  Fully distributed EM for very large datasets. 1184-1191
  
• 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
  
• Hengshuai Yao, Zhi-Qiang Liu:
  Preconditioned temporal difference learning. 1208-1215
  
• Jin Yu, S. V. N. Vishwanathan, Simon Günter, Nicol N. Schraudolph:
  A quasi-Newton approach to non-smooth convex optimization. 1216-1223
  
• Yisong Yue, Thorsten Joachims:
  Predicting diverse subsets using structural SVMs. 1224-1231
  
• 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
  
• Bin Zhao, Fei Wang, Changshui Zhang:
  Efficient multiclass maximum margin clustering. 1248-1255
  
• Jun Zhu, Eric P. Xing, Bo Zhang:
  Laplace maximum margin Markov networks. 1256-1263
  

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