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ORCID25th ICML 2008: Helsinki, Finland
- William W. Cohen, Andrew McCallum, Sam T. Roweis:
Machine Learning, Proceedings of the Twenty-Fifth International Conference (ICML 2008), Helsinki, Finland, June 5-9, 2008. ACM International Conference Proceeding Series 307, ACM 2008, 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 - 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 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 C. N. 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 J. 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, Alexander 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 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, Alexander 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 Singh:
Efficiently learning linear-linear exponential family predictive representations of state. 1176-1183 - Jason Andrew 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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