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Dale Schuurmans
2010 – today
- 2013
[i7]Dale Schuurmans, Finnegan Southey: Monte Carlo Inference via Greedy Importance Sampling. CoRR abs/1301.3890 (2013)- [i6]Russell Greiner, Adam J. Grove, Dale Schuurmans: Learning Bayesian Nets that Perform Well. CoRR abs/1302.1542 (2013)
- [i5]
- 2012
- [j21]Yi Shi, Maryam Hasan, Zhipeng Cai, Guohui Lin, Dale Schuurmans: Linear Coherent Bi-Clustering via Beam Searching and Sample Set Clustering. Discrete Math., Alg. and Appl. 4(2) (2012)
- [j20]Jiming Peng, Lopamudra Mukherjee, Vikas Singh, Dale Schuurmans, Linli Xu: An efficient algorithm for maximal margin clustering. J. Global Optimization 52(1): 123-137 (2012)
- [j19]Daniel J. Lizotte, Russell Greiner, Dale Schuurmans: An experimental methodology for response surface optimization methods. J. Global Optimization 53(4): 699-736 (2012)
- [j18]Martha White, Dale Schuurmans: Generalized Optimal Reverse Prediction. Journal of Machine Learning Research - Proceedings Track 22: 1305-1313 (2012)
- [j17]Shaojun Wang, Dale Schuurmans, Yunxin Zhao: The Latent Maximum Entropy Principle. TKDD 6(2): 8 (2012)
- [c90]James Neufeld, Yaoliang Yu, Xinhua Zhang, Ryan Kiros, Dale Schuurmans: Regularizers versus Losses for Nonlinear Dimensionality Reduction: A Factored View with New Convex Relaxations. ICML 2012
- [c89]Martha White, Yaoliang Yu, Xinhua Zhang, Dale Schuurmans: Convex Multi-view Subspace Learning. NIPS 2012: 1682-1690
- [c88]Yaoliang Yu, Özlem Aslan, Dale Schuurmans: A Polynomial-time Form of Robust Regression. NIPS 2012: 2492-2500
- [c87]Xinhua Zhang, Yaoliang Yu, Dale Schuurmans: Accelerated Training for Matrix-norm Regularization: A Boosting Approach. NIPS 2012: 2915-2923
- [c86]Yuhong Guo, Dale Schuurmans: Semi-supervised Multi-label Classification - A Simultaneous Large-Margin, Subspace Learning Approach. ECML/PKDD (2) 2012: 355-370
- [c85]Yi Shi, Xiaoping Liao, Xinhua Zhang, Guohui Lin, Dale Schuurmans: Sparse Learning Based Linear Coherent Bi-clustering. WABI 2012: 346-364
- [i4]Yaoliang Yu, Dale Schuurmans: Rank/Norm Regularization with Closed-Form Solutions: Application to Subspace Clustering. CoRR abs/1202.3772 (2012)
- [i3]Yuhong Guo, Dana F. Wilkinson, Dale Schuurmans: Maximum Margin Bayesian Networks. CoRR abs/1207.1382 (2012)
- [i2]Fletcher Lu, Dale Schuurmans: Monte Carlo Matrix Inversion Policy Evaluation. CoRR abs/1212.2471 (2012)
- [i1]Shaojun Wang, Dale Schuurmans, Fuchun Peng, Yunxin Zhao: Boltzmann Machine Learning with the Latent Maximum Entropy Principle. CoRR abs/1212.2514 (2012)
- 2011
- [j16]Li Cheng, Minglun Gong, Dale Schuurmans, Terry Caelli: Real-Time Discriminative Background Subtraction. IEEE Transactions on Image Processing 20(5): 1401-1414 (2011)
- [c84]Yuhong Guo, Dale Schuurmans: Adaptive Large Margin Training for Multilabel Classification. AAAI 2011
- [c83]Xinhua Zhang, Yaoliang Yu, Martha White, Ruitong Huang, Dale Schuurmans: Convex Sparse Coding, Subspace Learning, and Semi-Supervised Extensions. AAAI 2011
- [c82]
- [c81]
- [c80]Yaoliang Yu, Dale Schuurmans: Rank/Norm Regularization with Closed-Form Solutions: Application to Subspace Clustering. UAI 2011: 778-785
- 2010
- [c79]Yi Shi, Maryam Hasan, Zhipeng Cai, Guohui Lin, Dale Schuurmans: Linear Coherent Bi-cluster Discovery via Beam Detection and Sample Set Clustering. COCOA (1) 2010: 85-103
- [c78]Novi Quadrianto, Dale Schuurmans, Alex J. Smola: Distributed Flow Algorithms for Scalable Similarity Visualization. ICDM Workshops 2010: 1220-1227
- [c77]Yaoliang Yu, Min Yang, Linli Xu, Martha White, Dale Schuurmans: Relaxed Clipping: A Global Training Method for Robust Regression and Classification. NIPS 2010: 2532-2540
2000 – 2009
- 2009
- [j15]Yuxi Li, Csaba Szepesvári, Dale Schuurmans: Learning Exercise Policies for American Options. Journal of Machine Learning Research - Proceedings Track 5: 352-359 (2009)
- [j14]Min Yang, Yuxi Li, Dale Schuurmans: Dual Temporal Difference Learning. Journal of Machine Learning Research - Proceedings Track 5: 631-638 (2009)
- [c76]Yuhong Guo, Dale Schuurmans: A Reformulation of Support Vector Machines for General Confidence Functions. ACML 2009: 109-119
- [c75]Yi Shi, Zhipeng Cai, Guohui Lin, Dale Schuurmans: Linear Coherent Bi-cluster Discovery via Line Detection and Sample Majority Voting. COCOA 2009: 73-84
- [c74]
- [c73]Qinfeng Shi, Luping Zhou, Li Cheng, Dale Schuurmans: Discriminative Maximum Margin Image Object Categorization with Exact Inference. ICIG 2009: 232-237
- [c72]Linli Xu, Martha White, Dale Schuurmans: Optimal reverse prediction: a unified perspective on supervised, unsupervised and semi-supervised learning. ICML 2009: 143
- [c71]Novi Quadrianto, Tibério S. Caetano, John Lim, Dale Schuurmans: Convex Relaxation of Mixture Regression with Efficient Algorithms. NIPS 2009: 1491-1499
- [c70]Yaoliang Yu, Yuxi Li, Dale Schuurmans, Csaba Szepesvári: A General Projection Property for Distribution Families. NIPS 2009: 2232-2240
- [e2]Daphne Koller, Dale Schuurmans, Yoshua Bengio, Léon Bottou (Eds.): 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, 2008. Curran Associates, Inc. 2009
- [e1]Yoshua Bengio, Dale Schuurmans, John D. Lafferty, Christopher K. I. Williams, Aron Culotta (Eds.): Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, Vancouver, British Columbia, Canada. Curran Associates, Inc. 2009, ISBN 9781615679119
- 2008
- [c69]Qin Iris Wang, Dale Schuurmans, Dekang Lin: Semi-Supervised Convex Training for Dependency Parsing. ACL 2008: 532-540
- [c68]
- 2007
- [c67]Daniel J. Lizotte, Tao Wang, Michael H. Bowling, Dale Schuurmans: Automatic Gait Optimization with Gaussian Process Regression. IJCAI 2007: 944-949
- [c66]Qin Iris Wang, Dekang Lin, Dale Schuurmans: Simple Training of Dependency Parsers via Structured Boosting. IJCAI 2007: 1756-1762
- [c65]
- [c64]
- [c63]Tao Wang, Daniel J. Lizotte, Michael H. Bowling, Dale Schuurmans: Stable Dual Dynamic Programming. NIPS 2007
- [c62]Yuhong Guo, Dale Schuurmans: Learning Gene Regulatory Networks via Globally Regularized Risk Minimization. RECOMB-CG 2007: 83-95
- 2006
- [j13]Craig Boutilier, Relu Patrascu, Pascal Poupart, Dale Schuurmans: Constraint-based optimization and utility elicitation using the minimax decision criterion. Artif. Intell. 170(8-9): 686-713 (2006)
- [j12]Tibério S. Caetano, Terry Caelli, Dale Schuurmans, Dante Augusto Couto Barone: Graphical Models and Point Pattern Matching. IEEE Trans. Pattern Anal. Mach. Intell. 28(10): 1646-1663 (2006)
- [c61]Linli Xu, Koby Crammer, Dale Schuurmans: Robust Support Vector Machine Training via Convex Outlier Ablation. AAAI 2006: 536-542
- [c60]Tao Wang, Pascal Poupart, Michael H. Bowling, Dale Schuurmans: Compact, Convex Upper Bound Iteration for Approximate POMDP Planning. AAAI 2006: 1245-1252
- [c59]Feng Jiao, Shaojun Wang, Chi-Hoon Lee, Russell Greiner, Dale Schuurmans: Semi-Supervised Conditional Random Fields for Improved Sequence Segmentation and Labeling. ACL 2006
- [c58]Li Cheng, Shaojun Wang, Dale Schuurmans, Terry Caelli, S. V. N. Vishwanathan: An Online Discriminative Approach to Background Subtraction. AVSS 2006: 2
- [c57]Shaojun Wang, Shaomin Wang, Li Cheng, Russell Greiner, Dale Schuurmans: Stochastic Analysis of Lexical and Semantic Enhanced Structural Language Model. ICGI 2006: 97-111
- [c56]Linli Xu, Dana F. Wilkinson, Finnegan Southey, Dale Schuurmans: Discriminative unsupervised learning of structured predictors. ICML 2006: 1057-1064
- [c55]Li Cheng, S. V. N. Vishwanathan, Dale Schuurmans, Shaojun Wang, Terry Caelli: implicit Online Learning with Kernels. NIPS 2006: 249-256
- [c54]Chi-Hoon Lee, Shaojun Wang, Feng Jiao, Dale Schuurmans, Russell Greiner: Learning to Model Spatial Dependency: Semi-Supervised Discriminative Random Fields. NIPS 2006: 793-800
- [c53]Jiayuan Huang, Tingshao Zhu, Dale Schuurmans: Web Communities Identification from Random Walks. PKDD 2006: 187-198
- [c52]Jiayuan Huang, Tingshao Zhu, Russell Greiner, Dengyong Zhou, Dale Schuurmans: Information Marginalization on Subgraphs. PKDD 2006: 199-210
- [c51]Yuhong Guo, Dale Schuurmans: Convex Structure Learning for Bayesian Networks: Polynomial Feature Selection and Approximate Ordering. UAI 2006
- 2005
- [j11]Shaojun Wang, Dale Schuurmans, Fuchun Peng, Yunxin Zhao: Combining Statistical Language Models via the Latent Maximum Entropy Principle. Machine Learning 60(1-3): 229-250 (2005)
- [c50]
- [c49]Ali Ghodsi, Jiayuan Huang, Finnegan Southey, Dale Schuurmans: Tangent-Corrected Embedding. CVPR (1) 2005: 518-525
- [c48]Li Cheng, Feng Jiao, Dale Schuurmans, Shaojun Wang: Variational Bayesian image modelling. ICML 2005: 129-136
- [c47]Shaojun Wang, Shaomin Wang, Russell Greiner, Dale Schuurmans, Li Cheng: Exploiting syntactic, semantic and lexical regularities in language modeling via directed Markov random fields. ICML 2005: 948-955
- [c46]Tao Wang, Daniel J. Lizotte, Michael H. Bowling, Dale Schuurmans: Bayesian sparse sampling for on-line reward optimization. ICML 2005: 956-963
- [c45]Yuhong Guo, Russell Greiner, Dale Schuurmans: Learning Coordination Classifiers. IJCAI 2005: 714-721
- [c44]Craig Boutilier, Relu Patrascu, Pascal Poupart, Dale Schuurmans: Regret-based Utility Elicitation in Constraint-based Decision Problems. IJCAI 2005: 929-934
- [c43]
- 2004
- [j10]Fuchun Peng, Dale Schuurmans, Shaojun Wang: Augmenting Naive Bayes Classifiers with Statistical Language Models. Inf. Retr. 7(3-4): 317-345 (2004)
- [j9]Xiangji Huang, Fuchun Peng, Aijun An, Dale Schuurmans: Dynamic Web log session identification with statistical language models. JASIST 55(14): 1290-1303 (2004)
- [j8]Shaojun Wang, Dale Schuurmans, Fuchun Peng, Yunxin Zhao: Learning mixture models with the regularized latent maximum entropy principle. IEEE Transactions on Neural Networks 15(4): 903-916 (2004)
- [c42]Ali Ghodsi, Jiayuan Huang, Dale Schuurmans: Transformation-Invariant Embedding for Image Analysis. ECCV (4) 2004: 519-530
- [c41]
- 2003
- [j7]Xiangji Huang, Fuchun Peng, Dale Schuurmans, Nick Cercone, Stephen E. Robertson: Applying Machine Learning to Text Segmentation for Information Retrieval. Inf. Retr. 6(3-4): 333-362 (2003)
- [j6]Ali Ghodsi, Dale Schuurmans: Automatic basis selection techniques for RBF networks. Neural Networks 16(5-6): 809-816 (2003)
- [c40]Xiangji Huang, Fuchun Peng, Aijun An, Dale Schuurmans, Nick Cercone: Session Boundary Detection for Association Rule Learning Using n-Gram Language Models. Canadian Conference on AI 2003: 237-251
- [c39]Fletcher Lu, Dale Schuurmans: Model-Based Least-Squares Policy Evaluation. Canadian Conference on AI 2003: 342-352
- [c38]Shaojun Wang, Dale Schuurmans: Learning Continuous Latent Variable Models with Bregman Divergences. ALT 2003: 190-204
- [c37]Craig Boutilier, Relu Patrascu, Pascal Poupart, Dale Schuurmans: Constraint-Based Optimization with the Minimax Decision Criterion. CP 2003: 168-182
- [c36]Feng Jiao, Stan Z. Li, Heung-Yeung Shum, Dale Schuurmans: Face Alignment Using Statistical Models and Wavelet Features. CVPR (1) 2003: 321-327
- [c35]Fuchun Peng, Dale Schuurmans, Vlado Keselj, Shaojun Wang: Language Independent Authorship Attribution with Character Level N-Grams. EACL 2003: 267-274
- [c34]Fuchun Peng, Dale Schuurmans: Combining Naive Bayes and n-Gram Language Models for Text Classification. ECIR 2003: 335-350
- [c33]Shaojun Wang, Dale Schuurmans, Fuchun Peng, Yunxin Zhao: Learning Mixture Models with the Latent Maximum Entropy Principle. ICML 2003: 784-791
- [c32]Fuchun Peng, Xiangji Huang, Dale Schuurmans, Shaojun Wang: Text classification in Asian languages without word segmentation. IRAL 2003: 41-48
- [c31]Fuchun Peng, Dale Schuurmans, Shaojun Wang: Language and Task Independent Text Categorization with Simple Language Models. HLT-NAACL 2003
- [c30]
- [c29]Shaojun Wang, Dale Schuurmans, Fuchun Peng, Yunxin Zhao: Boltzmann Machine Learning with the Latent Maximum Entropy Principle. UAI 2003: 567-574
- 2002
- [j5]Yoshua Bengio, Dale Schuurmans: Guest Introduction: Special Issue on New Methods for Model Selection and Model Combination. Machine Learning 48(1-3): 5-7 (2002)
- [j4]Dale Schuurmans, Finnegan Southey: Metric-Based Methods for Adaptive Model Selection and Regularization. Machine Learning 48(1-3): 51-84 (2002)
- [c28]Relu Patrascu, Pascal Poupart, Dale Schuurmans, Craig Boutilier, Carlos Guestrin: Greedy Linear Value-Approximation for Factored Markov Decision Processes. AAAI/IAAI 2002: 285-291
- [c27]Pascal Poupart, Craig Boutilier, Relu Patrascu, Dale Schuurmans: Piecewise Linear Value Function Approximation for Factored MDPs. AAAI/IAAI 2002: 292-299
- [c26]Fuchun Peng, Xiangji Huang, Dale Schuurmans, Nick Cercone: Investigating the Relationship between Word Segmentation Performance and Retrieval Performance in Chinese IR. COLING 2002
- [c25]Carlos Guestrin, Relu Patrascu, Dale Schuurmans: Algorithm-Directed Exploration for Model-Based Reinforcement Learning in Factored MDPs. ICML 2002: 235-242
- [c24]Fletcher Lu, Relu Patrascu, Dale Schuurmans: Investigating the Maximum Likelihood Alternative to TD(lambda). ICML 2002: 403-410
- [c23]Finnegan Southey, Dale Schuurmans, Ali Ghodsi: Regularized Greedy Importance Sampling. NIPS 2002: 753-760
- [c22]Fuchun Peng, Xiangji Huang, Dale Schuurmans, Nick Cercone, Stephen E. Robertson: Using self-supervised word segmentation in Chinese information retrieval. SIGIR 2002: 349-350
- 2001
- [j3]Dale Schuurmans, Finnegan Southey: Local search characteristics of incomplete SAT procedures. Artif. Intell. 132(2): 121-150 (2001)
- [j2]Adam J. Grove, Nick Littlestone, Dale Schuurmans: General Convergence Results for Linear Discriminant Updates. Machine Learning 43(3): 173-210 (2001)
- [c21]
- [c20]Dale Schuurmans, Finnegan Southey, Robert C. Holte: The Exponentiated Subgradient Algorithm for Heuristic Boolean Programming. IJCAI 2001: 334-341
- [c19]
- [c18]Fuchun Peng, Dale Schuurmans: A Simple Closed-Class/Open-Class Factorization for Improved Language Modeling. NLPRS 2001: 145-152
- [c17]
- 2000
- [c16]Dale Schuurmans, Finnegan Southey: Local Search Characteristics of Incomplete SAT Procedures. AAAI/IAAI 2000: 297-302
- [c15]Dale Schuurmans, Finnegan Southey: An Adaptive Regularization Criterion for Supervised Learning. ICML 2000: 847-854
- [c14]Dale Schuurmans, Finnegan Southey: Monte Carlo inference via greedy importance sampling. UAI 2000: 523-532
1990 – 1999
- 1999
- [c13]Dale Schuurmans, Lloyd Greenwald: Efficient exploration for optimizing immediate reward. AAAI/IAAI 1999: 385-392
- [c12]
- 1998
- [c11]Adam J. Grove, Dale Schuurmans: Boosting in the Limit: Maximizing the Margin of Learned Ensembles. AAAI/IAAI 1998: 692-699
- 1997
- [j1]Dale Schuurmans: Characterizing Rational Versus Exponential learning Curves. J. Comput. Syst. Sci. 55(1): 140-160 (1997)
- [c10]
- [c9]Adam J. Grove, Nick Littlestone, Dale Schuurmans: General Convergence Results for Linear Discriminant Updates. COLT 1997: 171-183
- [c8]Dale Schuurmans, Lyle H. Ungar, Dean P. Foster: Characterizing the generalization performance of model selection strategies. ICML 1997: 340-348
- [c7]Russell Greiner, Adam J. Grove, Dale Schuurmans: Learning Bayesian Nets that Perform Well. UAI 1997: 198-207
- 1995
- [c6]
- [c5]
- [c4]
- 1992
- [c3]Russell Greiner, Dale Schuurmans: Learning an Optimally Accurate Representation System. ECAI Workshop on Knowledge Representation and Reasoning 1992: 145-159
- [c2]
1980 – 1989
- 1989
- [c1]Dale Schuurmans, Jonathan Schaeffer: Representational Difficulties with Classifier Systems. ICGA 1989: 328-333
Coauthor Index
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last updated on 2013-04-09 21:24 CEST by the dblp team