Dale Schuurmans - researchr alias

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2011

2010

Distributed Flow Algorithms for Scalable Similarity VisualizationNovi Quadrianto, Dale Schuurmans, Alex J. Smola. icdm 2010: 1220-1227 [doi]

Linear Coherent Bi-cluster Discovery via Beam Detection and Sample Set ClusteringYi Shi, Maryam Hasan, Zhipeng Cai, Guohui Lin, Dale Schuurmans. cocoa 2010: 85-103 [doi]

2009

A Reformulation of Support Vector Machines for General Confidence FunctionsYuhong Guo, Dale Schuurmans. acml 2009: 109-119 [doi]

Advances in Neural Information Processing Systems 21, Proceedings of the Twenty-Second Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, December 8-11, 2008Daphne Koller, Dale Schuurmans, Yoshua Bengio, Léon Bottou, editors, MIT Press, 2009.

Learning Exercise Policies for American OptionsYuxi Li, Csaba Szepesvári, Dale Schuurmans. jmlr, 5:352-359, 2009. [doi]

Discriminative Maximum Margin Image Object Categorization with Exact InferenceQinfeng Shi, Luping Zhou, Li Cheng, Dale Schuurmans. icig 2009: 232-237 [doi]

Linear Coherent Bi-cluster Discovery via Line Detection and Sample Majority VotingYi Shi, Zhipeng Cai, Guohui Lin, Dale Schuurmans. cocoa 2009: 73-84 [doi]

Optimal reverse prediction: a unified perspective on supervised, unsupervised and semi-supervised learningLinli Xu, Martha White, Dale Schuurmans. icml 2009: 143 [doi]

Fast normalized cut with linear constraintsLinli Xu, Wenye Li, Dale Schuurmans. cvpr 2009: 2866-2873 [doi]

Dual Temporal Difference LearningMin Yang, Yuxi Li, Dale Schuurmans. jmlr, 5:631-638, 2009. [doi]

2008

Policy Iteration for Learning an Exercise Policy for American OptionsYuxi Li, Dale Schuurmans. ewrl 2008: 165-178 [doi]

Semi-Supervised Convex Training for Dependency ParsingQin Iris Wang, Dale Schuurmans, Dekang Lin. acl 2008: 532-540 [doi]

2007

2006

Constraint-based optimization and utility elicitation using the minimax decision criterionCraig Boutilier, Relu Patrascu, Pascal Poupart, Dale Schuurmans. ai, 170(8-9):686-713, 2006. [doi]

Graphical Models and Point Pattern MatchingTibério S. Caetano, Terry Caelli, Dale Schuurmans, Dante Augusto Couto Barone. pami, 28(10):1646-1663, 2006. [doi]

implicit Online Learning with KernelsLi Cheng, S. V. N. Vishwanathan, Dale Schuurmans, Shaojun Wang, Terry Caelli. nips 2007: 249-256 [doi]

An Online Discriminative Approach to Background SubtractionLi Cheng, Shaojun Wang, Dale Schuurmans, Terry Caelli, S. V. N. Vishwanathan. avss 2006: 2 [doi]

Convex Structure Learning for Bayesian Networks: Polynomial Feature Selection and Approximate OrderingYuhong Guo, Dale Schuurmans. uai 2006: [doi]

Web Communities Identification from Random WalksJiayuan Huang, Tingshao Zhu, Dale Schuurmans. pkdd 2006: 187-198 [doi]

Information Marginalization on SubgraphsJiayuan Huang, Tingshao Zhu, Russell Greiner, Dengyong Zhou, Dale Schuurmans. pkdd 2006: 199-210 [doi]

Semi-Supervised Conditional Random Fields for Improved Sequence Segmentation and LabelingFeng Jiao, Shaojun Wang, Chi-Hoon Lee, Russell Greiner, Dale Schuurmans. acl 2006: [doi]

Learning to Model Spatial Dependency: Semi-Supervised Discriminative Random FieldsChi-Hoon Lee, Shaojun Wang, Feng Jiao, Dale Schuurmans, Russell Greiner. nips 2007: 793-800 [doi]

Compact, Convex Upper Bound Iteration for Approximate POMDP PlanningTao Wang, Pascal Poupart, Michael H. Bowling, Dale Schuurmans. AAAI 2006:

Stochastic Analysis of Lexical and Semantic Enhanced Structural Language ModelShaojun Wang, Shaomin Wang, Li Cheng, Russell Greiner, Dale Schuurmans. icgi 2006: 97-111 [doi]

Discriminative unsupervised learning of structured predictorsLinli Xu, Dana F. Wilkinson, Finnegan Southey, Dale Schuurmans. icml 2006: 1057-1064 [doi]

Robust Support Vector Machine Training via Convex Outlier AblationLinli Xu, Koby Crammer, Dale Schuurmans. AAAI 2006:

2005

Regret-based Utility Elicitation in Constraint-based Decision ProblemsCraig Boutilier, Relu Patrascu, Pascal Poupart, Dale Schuurmans. IJCAI 2005: 929-934 [doi]

Variational Bayesian image modellingLi Cheng, Feng Jiao, Dale Schuurmans, Shaojun Wang. icml 2005: 129-136 [doi]

Tangent-Corrected EmbeddingAli Ghodsi, Jiayuan Huang, Finnegan Southey, Dale Schuurmans. cvpr 2005: 518-525 [doi]

Maximum Margin Bayesian NetworksYuhong Guo, Dana F. Wilkinson, Dale Schuurmans. uai 2005: 233-242 [doi]

Learning Coordination ClassifiersYuhong Guo, Russell Greiner, Dale Schuurmans. IJCAI 2005: 714-721 [doi]

Bayesian sparse sampling for on-line reward optimizationTao Wang, Daniel J. Lizotte, Michael H. Bowling, Dale Schuurmans. icml 2005: 956-963 [doi]

Exploiting syntactic, semantic and lexical regularities in language modeling via directed Markov random fieldsShaojun Wang, Shaomin Wang, Russell Greiner, Dale Schuurmans, Li Cheng. icml 2005: 948-955 [doi]

Combining Statistical Language Models via the Latent Maximum Entropy PrincipleShaojun Wang, Dale Schuurmans, Fuchun Peng, Yunxin Zhao. ml, 60(1-3):229-250, 2005. [doi]

Unsupervised and Semi-Supervised Multi-Class Support Vector MachinesLinli Xu, Dale Schuurmans. AAAI 2005: 904-910

2004

2003

Constraint-Based Optimization with the Minimax Decision CriterionCraig Boutilier, Relu Patrascu, Pascal Poupart, Dale Schuurmans. cp 2003: 168-182 [doi]

Automatic basis selection techniques for RBF networksAli Ghodsi, Dale Schuurmans. NN, 16(5-6):809-816, 2003. [doi]

Applying Machine Learning to Text Segmentation for Information RetrievalXiangji Huang, Fuchun Peng, Dale Schuurmans, Nick Cercone, Stephen E. Robertson. ir, 6(3-4):333-362, 2003.

Session Boundary Detection for Association Rule Learning Using n-Gram Language ModelsXiangji Huang, Fuchun Peng, Aijun An, Dale Schuurmans, Nick Cercone. ai 2003: 237-251 [doi]

Face Alignment Using Statistical Models and Wavelet FeaturesFeng Jiao, Stan Z. Li, Heung-Yeung Shum, Dale Schuurmans. cvpr 2003: 321-327 [doi]

Monte Carlo Matrix Inversion Policy EvaluationFletcher Lu, Dale Schuurmans. uai 2003: 386-393

Model-Based Least-Squares Policy EvaluationFletcher Lu, Dale Schuurmans. ai 2003: 342-352 [doi]

Language Independent Authorship Attribution with Character Level N-GramsFuchun Peng, Dale Schuurmans, Vlado Keselj, Shaojun Wang. eacl 2003: 267-274 [doi]

Text classification in Asian languages without word segmentationFuchun Peng, Xiangji Huang, Dale Schuurmans, Shaojun Wang. iral 2003: 41-48 [doi]

Combining Naive Bayes and n-Gram Language Models for Text ClassificationFuchun Peng, Dale Schuurmans. ecir 2003: 335-350 [doi]

Language and Task Independent Text Categorization with Simple Language ModelsFuchun Peng, Dale Schuurmans, Shaojun Wang. naacl 2003: [doi]

Learning Mixture Models with the Latent Maximum Entropy PrincipleShaojun Wang, Dale Schuurmans, Fuchun Peng, Yunxin Zhao. icml 2003: 784-791

Learning Continuous Latent Variable Models with Bregman DivergencesShaojun Wang, Dale Schuurmans. alt 2003: 190-204 [doi]

Boltzmann Machine Learning with the Latent Maximum Entropy PrincipleShaojun Wang, Dale Schuurmans, Fuchun Peng, Yunxin Zhao. uai 2003: 567-574

2002

Guest Introduction: Special Issue on New Methods for Model Selection and Model CombinationYoshua Bengio, Dale Schuurmans. ml, 48(1-3):5-7, 2002.

Algorithm-Directed Exploration for Model-Based Reinforcement Learning in Factored MDPsCarlos Guestrin, Relu Patrascu, Dale Schuurmans. icml 2002: 235-242

Investigating the Maximum Likelihood Alternative to TD(lambda)Fletcher Lu, Relu Patrascu, Dale Schuurmans. icml 2002: 403-410

Greedy Linear Value-Approximation for Factored Markov Decision ProcessesRelu Patrascu, Pascal Poupart, Dale Schuurmans, Craig Boutilier, Carlos Guestrin. AAAI 2002: 285-291

Investigating the Relationship between Word Segmentation Performance and Retrieval Performance in Chinese IRFuchun Peng, Xiangji Huang, Dale Schuurmans, Nick Cercone. COLING 2002: [doi]

Using self-supervised word segmentation in Chinese information retrievalFuchun Peng, Xiangji Huang, Dale Schuurmans, Nick Cercone, Stephen E. Robertson. sigir 2002: 349-350 [doi]

Piecewise Linear Value Function Approximation for Factored MDPsPascal Poupart, Craig Boutilier, Relu Patrascu, Dale Schuurmans. AAAI 2002: 292-299

Metric-Based Methods for Adaptive Model Selection and RegularizationDale Schuurmans, Finnegan Southey. ml, 48(1-3):51-84, 2002.

Regularized Greedy Importance SamplingFinnegan Southey, Dale Schuurmans, Ali Ghodsi. nips 2003: 753-760 [doi]

2001

2000

Monte Carlo inference via greedy importance samplingDale Schuurmans, Finnegan Southey. uai 2000: 523-532 [doi]

An Adaptive Regularization Criterion for Supervised LearningDale Schuurmans, Finnegan Southey. icml 2000: 847-854

Local Search Characteristics of Incomplete SAT ProceduresDale Schuurmans, Finnegan Southey. AAAI 2000: 297-302

1999

Greedy Importance SamplingDale Schuurmans. nips 2000: 596-602 [doi]

Efficient exploration for optimizing immediate rewardDale Schuurmans, Lloyd Greenwald. AAAI 1999: 385-392

1998

Boosting in the Limit: Maximizing the Margin of Learned EnsemblesAdam J. Grove, Dale Schuurmans. AAAI 1998: 692-699

1997

1995

Sequential PAC LearningDale Schuurmans, Russell Greiner. colt 1995: 377-384 [doi]

Practical PAC LearningDale Schuurmans, Russell Greiner. IJCAI 1995: 1169-1177

Characterizing rational versus exponential learning curvesDale Schuurmans. eurocolt 1995: 272-286

1992

Learning an Optimally Accurate Representation SystemRussell Greiner, Dale Schuurmans. ecai 1994: 145-159

Learning Useful Horn ApproximationsRussell Greiner, Dale Schuurmans. kr 1992: 383-392

1989

Representational Difficulties with Classifier SystemsDale Schuurmans, Jonathan Schaeffer. icga 1989: 328-333