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Publications by Schuurmans, Dale

In Journal (refereed)

1. M. Elgendi, S. Kumar, L. Guo, J. Rutledge, J. Coe, R. Zemp, D. Schuurmans, I. Adatia. "Detection of heart sounds in children with and without pulmonary hypertension---Daubecheis wavelets approach". PLoS One, 10(12), pp 1-22, December 2015. view
2. M. Elgendi, P. Bobhate, S. Jain, L. Guo, S. Kumar, J. Rutledge, Y. Coe, R. Zemp, D. Schuurmans, I. Adatia. "The unique heart sound signature of children with pulmonary artery hypertension". Pulmonary Circulation, 5(4), pp 631-639, December 2015. view
3. M. Elgendi, P. Bobhate, S. Jain, J. Rutledge, J. Coe, R. Zemp, D. Schuurmans, I. Adatia. "Time-domain analysis of heart sound intensity in children with and without pulmonary artery hypertension: a pilot study using a digital stethoscope". Pulmonary Circulation, 4(4), pp Elgendi, M., Bobhate, P., Jain, S., Rutledge, J., Coe, J. Y., Zemp, R., Schuurmans, D., & Adatia, I. (2014). Time-domain analysis of heart sound intensity in children with and without pulmonary artery hypertension: a pilot study using a digital steth, December 2014. view
4. M. Elgendi, P. Bobhate, S. Jain, L. Guo, J. Rutledge, Y. Coe, R. Zemp, D. Schuurmans, I. Adatia. "Spectral analysis of the heart sounds in children with and without pulmonary artery hypertension". International Journal of Cardiology, 173(1), pp 92-99, April 2014. PDFview
5. S. Wang, S. Wang, L. Cheng, R. Greiner, D. Schuurmans. "Exploiting Syntactic, Semantic and Lexical Regularities in Language Modeling via Directed Markov Random Fields". Computational Intelligence, 29(4), pp 649–679, November 2013. PDFview
6. D. Lizotte, R. Greiner, D. Schuurmans. "An Experimental Methodology for Response Surface Optimization Methods". Journal of Global Optimization, 53(4), pp 699-736, June 2012. view
7. C. Boutilier, R. Patrascu, P. Poupart, D. Schuurmans. "Constraint-Based Optimization and Utility Elicitation Using the Minimax Decision Criterion". Artificial Intelligence (AIJ), 170(8-9), pp 686-713, January 2006. view
8. T. Caetano, T. Caelli, D. Schuurmans, D. Barone. "Graphical Models and Point Pattern Matching". IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(10), January 2006. PDFview
9. S. Wang, D. Schuurmans, F. Peng, Y. Zhao. "Combining Statistical Language Models Via the Latent Maximum Entropy Principle". Machine Learning Journal (MLJ), 60(1-3), pp 229-250, September 2005. view
10. S. Wang, D. Schuurmans, F. Peng, Y. Zhao. "Learning mixture models with the regularized maximum entropy principle". IEEE Transactions on Neural Networks, 15(4), pp 903-916, January 2005. PSview
11. X. Huang, F. Peng, A. An, D. Schuurmans. "Dynamic Web Log Session Identification With Statistical Language Models". Journal of the American Society for Information Science and Technology (JASTIS), 55(14), pp 1290-1303, December 2004. view
12. X. Huang, F. Peng, D. Schuurmans, N. Cercone, S. Robertson. "Applying Machine Learning to Text Segmentation for Information Retrieval". Information Retrieval (IR), 6(3), pp 333-362, September 2003. view
13. F. Peng, D. Schuurmans, S. Wang. "Augmenting Naive Bayes Classifiers with Statistical Language Models". Information Retrieval (IR), September 2003. view
14. A. Ghodsi, D. Schuurmans. "Automatic basis selection techniques for RBF networks". Neural Networks, 16(5-6), pp 809-816, June 2003. view
15. D. Schuurmans, F. Southey. "Metric-Based Methods for Adaptive Model Selection and Regularization". Machine Learning Journal (MLJ), 48(1-3), pp 51-84, January 2002. PDFview
16. A. Grove, N. Littlestone, D. Schuurmans. "General Convergence Results for Linear Discriminant Updates". Machine Learning Journal (MLJ), 43(3), pp 179-210, December 2001. PDFview
17. D. Schuurmans, F. Southey. "Local search characteristics of incomplete SAT procedures". Artificial Intelligence (AIJ), 132(2), pp 121--150, June 2001. PDFview
18. D. Schuurmans. "Characterizing Rational Versus Exponential Learning Curves". Journal of Computer System Sciences, 55(1), pp 140-160, March 1997. view

In Conference (refereed)

19. M. Karami, M. White, D. Schuurmans, C. Szepesvari. "Multi-view Matrix Factorization for Linear Dynamical System Estimation". NIPS Workshop on Machine Learning and Games, (ed: Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, Roman Garnett), pp 7092-7101, December 2017. PDFview
20. D. Schuurmans, M. Zinkevich. "Deep Learning Games". Neural Information Processing Systems (NIPS), (ed: D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, R. Garnett), pp 1678-1686, December 2016. PDFview
21. M. Norouzi, S. Bengio, Z. Chen, N. Jaitly, M. Schuster, Y. Wu, D. Schuurmans. "Reward Augmented Maximum Likelihood for Neural Structured Prediction". Neural Information Processing Systems (NIPS), (ed: D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, R. Garnett), pp 1723-1731, December 2016. PDFview
22. H. Cheng, Y. Yu, X. Zhang, E. Xing, D. Schuurmans. "Scalable and sound low rank tensor learning". Artificial Intelligence and Statistics, (ed: Arthur Gretton, Christian C. Robert), pp 1114-1123, May 2016. view
23. S. Ravanbakhsh, B. Poczos, J. Schneider, D. Schuurmans, R. Greiner. "Stochastic Neural Networks with Monotonic Activation Functions". Artificial Intelligence and Statistics, (ed: Arthur Gretton, Christian C. Robert), pp 809-818, May 2016. PDFview
24. O. Aslan, X. Zhang, D. Schuurmans. "Convex Deep Learning via Normalized Kernels". Neural Information Processing Systems (NIPS), (ed: Zoubin Ghahramani, Max Welling, Corinna Cortes, Neil D. Lawrence, Kilian Q. Weinberger), pp 3275-3283, December 2015. PDFview
25. F. Mirzazadeh, S. Ravanbakhsh, N. Ding, D. Schuurmans. "Embedding Inference for Structured Multilabel Prediction". Neural Information Processing Systems (NIPS), (ed: C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, R. Garnett), pp 3555-3563, December 2015. PDFview
26. X. Li, Y. Guo, D. Schuurmans. "Semi-Supervised Zero-Shot Classification with Label Representation Learning". IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, December 2015. PDFview
27. K. Abou-Moustafa, D. Schuurmans. "Generalization in Unsupervised Learning". European Conference on Machine Learning (ECML), (ed: Appice A., Rodrigues P., Santos Costa V., Soares C., Gama J., Jorge A.), pp 300-317, September 2015. PDFview
28. F. Mirzazadeh, M. White, A. György, D. Schuurmans. "Scalable metric learning for co-embedding". European Conference on Machine Learning (ECML), (ed: Appice A., Rodrigues P., Santos Costa V., Soares C., Gama J., Jorge A.), pp 625-642, September 2015. PDFview
29. J. Wen, R. Greiner, D. Schuurmans. "Correcting Covariate Shift with Frank-Wolfe Algorithm". International Joint Conference on Artificial Intelligence (IJCAI), (ed: Qiang Yang, Michael Wooldridge), pp 1010-1016, July 2015. view
30. J. Neufeld, D. Schuurmans, M. Bowling. "Variance Reduction via Antithetic Markov Chains". Artificial Intelligence and Statistics, (ed: Guy Lebanon, S. V. N. Vishwanathan), pp 708-716, May 2015. PDFview
31. M. White, J. Wen, M. Bowling, D. Schuurmans. "Optimal Estimation of Multivariate ARMA Models". National Conference on Artificial Intelligence (AAAI), (ed: Blai Bonet, Sven Koenig), pp 3080-3086, January 2015. PDFview
32. F. Mirzazadeh, Y. Guo, D. Schuurmans. "Convex co-embedding". National Conference on Artificial Intelligence (AAAI), pp 1989-1996, July 2014. PDFview
33. J. Neufeld, A. Gyorgy, C. Szepesvari, D. Schuurmans. "Adaptive Monte Carlo via bandit allocation". International Conference on Machine Learning (ICML), (ed: Eric P. Xing, Tony Jebara), pp 1944-1952, June 2014. PDFview
34. Ã. Aslan, H. Cheng, X. Zhang, D. Schuurmans. "Convex Two-Layer Modeling". Neural Information Processing Systems (NIPS), (ed: C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahraman, K. Q. Weinberger), pp 2985-2993, December 2013. PDFview
35. K. Abou-Moustafa, F. Ferrie, D. Schuurmans. "Divergence Based Graph Estimation for Manifold Learning". IEEE Global Conference on Signal and Information Processing, December 2013. PDFview
36. X. Zhang, Y. Yu, D. Schuurmans. "Polar Operators for Structured Sparse Estimation". Neural Information Processing Systems (NIPS), (ed: C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, K. Q. Weinberger), pp 82-90, December 2013. PDFview
37. K. Abou-Moustafa, D. Schuurmans, F. Ferrie. "Learning a metric space for neighborhood topology estimation: Applications to manifold learning". Asian Conference on Machine Learning, (ed: Cheng Soon Ong, Tu Bao Ho), pp 341-356, November 2013. PDFview
38. K. Abou-Moustafa, D. Schuurmans, F. Ferrie. "Learning a metric space for neighbourhood topology estimation. Application to manifold learning". Asian Conference on Machine Learning, (ed: Cheng Soon Ong and Tu Bao Ho), pp 1-16, November 2013. PDFview
39. Y. Guo, D. Schuurmans. "Multi-label Classification with Output Kernels". European Conference on Machine Learning (ECML), pp 417-432, September 2013. PDFview
40. H. Cheng, X. Zhang, D. Schuurmans. "Convex Relaxations of Bregman Divergence Clustering". Conference on Uncertainty in Artificial Intelligence (UAI), pp 162-171, August 2013. PDFview
41. Y. Yu, H. Cheng, D. Schuurmans, C. Szepesvari. "Characterizing the representer theorem". International Conference on Machine Learning (ICML), (ed: Sanjoy Dasgupta, David McAllester), pp 570-578, June 2013. PDFview
42. Y. Shi, X. Zhang, X. Liao, G. Lin, D. Schuurmans. "Protein-chemical interaction prediction via a kernelized sparse learning SVM". Pacific Symposium on Biocomputing, (ed: Russ B. Altman, A. Keith Dunker, Lawrence Hunter, Tiffany Murray, Teri E. Klein), pp 41-52, January 2013. PDFview
43. M. White, Y. Yu, X. Zhang, D. Schuurmans. "Convex Multi-view Subspace Learning". NIPS Workshop on Machine Learning and Games, (ed: Peter L. Bartlett, Fernando C. N. Pereira, Christopher J. C. Burges, Leon Bottou, Kilian Q. Weinberger), pp 1682-1690, December 2012. PDFview
44. M. White, D. Schuurmans. "Generalized Optimal Reverse Prediction". Artificial Intelligence and Statistics, (ed: Neil D. Lawrence, Mark A. Girolami), pp 1305-1313, April 2012. PDFview
45. Y. Yu, Y. Li, C. Szepesvari, D. Schuurmans. "A general projection property for distribution families". Neural Information Processing Systems (NIPS), December 2009. view
46. N. Quadrianto, T. Caetano, J. Lim, D. Schuurmans. "Convex relaxation of mixture regression with efficient algorithms". Neural Information Processing Systems (NIPS), December 2009. view
47. Y. Guo, D. Schuurmans. "A reformulation of support vector machines for general confidence functions". Asian Conference on Machine Learning, November 2009. view
48. L. Xu, M. White, D. Schuurmans. " Optimal reverse prediction: a unified perspective on supervised, unsupervised and semi-supervised learning". International Conference on Machine Learning (ICML), June 2009. view
49. L. Xu, W. Li, D. Schuurmans. "Fast normalized cut with linear constraints". Computer Vision and Pattern Recognition (CVPR), June 2009. view
50. Y. Shi, Z. Cai, G. Lin, D. Schuurmans. "Linear-coherent bi-cluster discovery via line detection and sample majority voting". International Conference on Combinatorial Optimization and Applications, June 2009. view
51. M. Yang, Y. Li, D. Schuurmans. "Dual temporal difference learning". Artificial Intelligence and Statistics, April 2009. view
52. Y. Li, C. Szepesvari, D. Schuurmans. "Learning exercise policies for American options". Artificial Intelligence and Statistics, April 2009. view
53. Q. Wang, D. Lin, D. Schuurmans. "Semi-supervised convex training for dependency parsing". International Conference on Computational Linguistics and the Association for Computational Linguist, June 2008. view
54. Y. Guo, D. Schuurmans. "Convex relaxations of latent variable training". Neural Information Processing Systems (NIPS), December 2007. view
55. Y. Guo, D. Schuurmans. "Discriminative batch mode active learning". Neural Information Processing Systems (NIPS), December 2007. view
56. T. Wang, D. Lizotte, M. Bowling, D. Schuurmans. "Stable dual dynamic programming". Neural Information Processing Systems (NIPS), December 2007. view
57. T. Wang, M. Bowling, D. Schuurmans. "Dual Representations for Dynamic Programming and Reinforcement Learning". Symposium on Approximate Dynamic Programming and Reinforcement Learning, pp 44-51, March 2007. PDFview
58. Q. Wang, D. Lin, D. Schuurmans. "Simple training of dependency parsers via structured boosting". International Joint Conference on Artificial Intelligence (IJCAI), Hyderabad, India, March 2007. PDFview
59. D. Lizotte, T. Wang, M. Bowling, D. Schuurmans. "Automatic Gait Optimization with Gaussian Process Regression". International Joint Conference on Artificial Intelligence (IJCAI), Hyderabad, India, January 2007. view
60. C. Lee, S. Wang, F. Jiao, D. Schuurmans, R. Greiner. "Learning to Model Spatial Dependency: Semi-Supervised Discriminative Random Fields". Neural Information Processing Systems (NIPS), December 2006. PDFview
61. J. Huang, T. Zhu, R. Greiner, D. Zhou, D. Schuurmans. "Information Marginalization on Subgraphs". European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), Berlin, Germany, September 2006. PDFview
62. S. Wang, S. Wang, L. Cheng, R. Greiner, D. Schuurmans. "Stochastic Analysis of Lexical and Semantic Enhanced Structural Language Model". International Colloquium on Grammatical Inference (ICGI), Chofu, Tokyo, Japan, pp 97-111, September 2006. PDFview
63. F. Jiao, S. Wang, C. Lee, R. Greiner, D. Schuurmans. "Semi-Supervised Conditional Random Fields for Improved Sequence Segmentation and Labeling". International Conference on Computational Linguistics and the Association for Computational Linguist, July 2006. PDFview
64. Q. Wang, C. Cherry, D. Lizotte, D. Schuurmans. "Improved Large Margin Dependency Parsing Via Local Constraints and Laplacian Regularization". Computational Natural Language Learning (CONLL), June 2006. PDFview
65. L. Cheng, S. Wang, D. Schuurmans, T. Caelli, S. Vishwantathan. "An online discriminative approach to background subtraction". IEEE, January 2006. PDFview
66. T. Wang, P. Poupart, M. Bowling, D. Schuurmans. "Compact, convex upper bound iteration for approximate POMDP planning". National Conference on Artificial Intelligence (AAAI), Boston, Massachusetts, USA, pp 1245-1251, January 2006. PDFview
67. Y. Guo, D. Schuurmans. "Convex structure learning for Bayesian networks: polynomial feature selection and approximate ordering". Conference on Uncertainty in Artificial Intelligence (UAI), January 2006. PDFview
68. L. Xu, D. Wilkinson, F. Southey, D. Schuurmans. "Discriminative Unsupervised Learning of Structured Predictors". International Conference on Machine Learning (ICML), Pittsburgh, January 2006. PDFview
69. L. Cheng, S. Vishwantathan, D. Schuurmans, S. Wang, T. Caelli. "Implicit Online Learning with Kernels". Neural Information Processing Systems (NIPS), January 2006. PDFview
70. F. Jiao, J. Xu, L. Yu, D. Schuurmans. "Protein fold recognition using the gradient boost algorithm". Computational Systems Bioinformatics Conference (CSB), January 2006. PDFview
71. L. Xu, K. Crammer, D. Schuurmans. "Robust Support Vector Machine Training Via Convex Outlier Ablation". National Conference on Artificial Intelligence (AAAI), Boston, Massachusetts, USA, January 2006. PDFview
72. J. Huang, T. Zhu, D. Schuurmans. "Web community identification from random walks". European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), Berlin, Germany, January 2006. PDFview
73. S. Wang, S. Wang, R. Greiner, D. Schuurmans, L. Cheng. "Exploiting Syntactic, Semantic and Lexical Regularities in Language Modeling via Directed Markov Random Fields". International Conference on Machine Learning (ICML), Bonn, Germany, pp 953-960, August 2005. PSview
74. Y. Guo, R. Greiner, D. Schuurmans. "Learning Coordination Classifiers". International Joint Conference on Artificial Intelligence (IJCAI), Edinburgh, Scotland, August 2005. PDFview
75. C. Boutilier, R. Patrascu, P. Poupart, D. Schuurmans. "Regret-Based Utility Elicitation in Constraint-Based Decision Problems". International Joint Conference on Artificial Intelligence (IJCAI), Edinburgh, Scotland, August 2005. PDFview
76. A. Ghodsi, J. Huang, F. Southey, D. Schuurmans. "Tangent Corrected Embedding". Computer Vision and Pattern Recognition (CVPR), June 2005. PDFview
77. T. Wang, D. Lizotte, M. Bowling, D. Schuurmans. "Bayesian Sparse Sampling for On-Line Reward Optimization". International Conference on Machine Learning (ICML), Bonn, Germany, pp 961-968, January 2005. PDFview
78. Q. Wang, D. Schuurmans. "Improved estimation for unsupervised part-of-speech tagging". IEEE, January 2005. PDFview
79. Y. Guo, D. Wilkinson, D. Schuurmans. "Maximum Margin Bayesian Networks". Conference on Uncertainty in Artificial Intelligence (UAI), Edinburgh, Scotland, January 2005. view
80. L. Xu, D. Schuurmans. "Unsupervised and Semi-Supervised Multi-Class Support Vector Machines". National Conference on Artificial Intelligence (AAAI), Pittsburgh, January 2005. view
81. L. Cheng, F. Jiao, D. Schuurmans, S. Wang. "Variational Bayesian Image Modelling". International Conference on Machine Learning (ICML), Bonn, Germany, January 2005. view
82. A. Ghodsi, J. Huang, D. Schuurmans. "Transformation-Invariant Embedding for image analysis". European Conference on Computer Vision (ECCV), Prague, Czech Republic, May 2004. PSview
83. L. Xu, J. Neufeld, B. Larson, D. Schuurmans. "Maximum Margin Clustering". Neural Information Processing Systems (NIPS), Vancouver, British Columbia, Canada, January 2004. view
84. S. Wang, D. Schuurmans. "Learning Continuous Latent Variable Models With Bregman Divergences". ICASSP, October 2003. PSview
85. S. Wang, D. Schuurmans, F. Peng, Y. Zhao. "Boltzmann Machine Learning With the Latent Maximum Entropy Principle". Conference on Uncertainty in Artificial Intelligence (UAI), Acapulco, Mexico, August 2003. PSview
86. S. Wang, D. Schuurmans, F. Peng, Y. Zhao. "Learning mixture models with the regularized latent maximum entropy principle". International Conference on Machine Learning (ICML), Washington, DC USA, August 2003. PDFview
87. A. Ghodsi, D. Schuurmans. "Automatic Basis Selectsion for RBF Networks". IJCNN, July 2003. view
88. A. Ghodsi, D. Schuurmans. "Automatic basis selection for RBF networks using Stein's unbiased risk estimator". IJCNN, June 2003. PDFview
89. A. Ghodsi, D. Schuurmans. "Automatic Complexity Control for System Identification". Fuzzy Systems Association World Congress(IFSA), June 2003. PDFview
90. C. Boutilier, R. Patrascu, P. Poupart, D. Schuurmans. "Constraint-based optimization and elicitation with the minimax decision criterion". International Joint Conference on Artificial Intelligence (IJCAI), Acapulco, Mexico, June 2003. view
91. F. Lu, D. Schuurmans. "Model-Based Least-Squares Policy Evalulation". Canadian Conference on Artificial Intelligence (CAI), Halifax, Nova Scotia, Canada, June 2003. PSview
92. F. Peng, D. Schuurmans, S. Wang. "Language and Task Independent Text Categorization With Simple Language Models". HLT-NAACL, May 2003. PDFview
93. F. Peng, D. Schuurmans, S. Wang. "Language independent authorship attribution using character level language models". EACL, April 2003. PSview
94. S. Wang, D. Schuurmans, F. Peng, Y. Zhao. "Semantic N-Gram Language Modelling With the Latent Maximum Entropy Principle". ICASSP, April 2003. view
95. F. Peng, D. Schuurmans. "Combining Naive Bayes and n-Gram Language Models for Text Classification". ECIR, January 2003. PSview
96. S. Wang, D. Schuurmans, F. Peng. "Latent Maximum Entropy Approach for Semantic N-Gram Language Modeling". International Workshop on Artificial Intelligence and Statistics (AISTATS), January 2003. PDFview
97. F. Lu, D. Schuurmans. "Monte Carlo matrix inversion policy evaluation". Conference on Uncertainty in Artificial Intelligence (UAI), Acapulco, Mexico, January 2003. PSview
98. X. Huang, F. Peng, A. An, D. Schuurmans, N. Cercone. "Session Boundary Detection for Association Rule Learning Using n-Gram Language Models". Canadian Conference on Artificial Intelligence (CAI), Halifax, Nova Scotia, Canada, January 2003. PDFview
99. F. Peng, X. Huang, D. Schuurmans, N. Cercone. "Investigating the Relationship Between Word Segmentation Performance and Retrieval Performance in Chinese IR". Conference on Computational Linguistics (COLING), Taipei, August 2002. PDFview
100. C. Guestrin, R. Patrascu, D. Schuurmans. "Algorithm-Directed Exploration for Model-Based Reinforcement Learning in Factored MDPs". International Conference on Machine Learning (ICML), Sydney Australia, July 2002. PDFview
101. G. Elidan, M. Ninio, N. Friedman, D. Schuurmans. "Data Perturbation for Escaping Local Maxima in Learning". National Conference on Artificial Intelligence (AAAI), Edmonton Alberta, July 2002. PDFview
102. R. Patrascu, P. Poupart, D. Schuurmans, C. Boutilier, C. Guestrin. "Greedy Linear Value-Approximation for Factored Markov Decision Processes". National Conference on Artificial Intelligence (AAAI), Edmonton Alberta, July 2002. PDFview
103. P. Poupart, C. Boutilier, R. Patrascu, D. Schuurmans. "Piecewise Linear Value Function Approximation for Factored MDPs". National Conference on Artificial Intelligence (AAAI), Edmonton Alberta, July 2002. PDFview
104. S. Wang, R. Rosenfeld, Y. Zhao, D. Schuurmans. "The latent maximum entropy principle". International Symposium on Information Theory (ISIT), June 2002. view
105. F. Lu, R. Patrascu, D. Schuurmans. "Investigating the Maximum Likelihood Alternative to TD()". Neural Information Processing Systems (NIPS), Vancouver, British Columbia, Canada, January 2002. PDFview
106. F. Southey, D. Schuurmans, A. Ghodsi. "Regularized Greedy Importance Sampling". Neural Information Processing Systems (NIPS), Vancouver, British Columbia, Canada, January 2002. PDFview
107. F. Peng, X. Huang, D. Schuurmans, N. Cercone, S. Robertson. "Using Self-Supervised Word Segmentation in Chinese Information Retrieval". SIGIR, January 2002. PDFview
108. F. Peng, D. Schuurmans. "A Simple Closed-Class/Open-Class Factorization for Improved Language Modeling". Natural Language Processing Pacific Rim Symposium, December 2001. PDFview
109. D. Schuurmans, R. Patrascu. "Direct Value-Approximation for Factored MDPs". Neural Information Processing Systems (NIPS), Vancouver, British Columbia, Canada, December 2001. PDFview
110. F. Peng, D. Schuurmans. "A Hierarchical EM Approach to Word Segmentation". Natural Language Processing Pacific Rim Symposium, November 2001. PDFview
111. F. Peng, D. Schuurmans. "Self-Supervised Chinese Word Segmentation". International Joint Conference on Artificial Intelligence (IJCAI), September 2001. PDFview
112. D. Schuurmans, F. Southey, R. Holte. "The Exponentiated Subgradient Algorithm for Heuristic Boolean Programming". International Joint Conference on Artificial Intelligence (IJCAI), pp 334-341, August 2001. PDFview
113. D. Schuurmans, A. Bistritz, F. Southey. "Monte Carlo Inference Via Greedy Importance Sampling". Conference on Uncertainty in Artificial Intelligence (UAI), July 2000. PDFview
114. D. Schuurmans, F. Southey. "An adaptive regularization criterion for supervised learning". International Conference on Machine Learning (ICML), Stanford University, June 2000. PDFview
115. D. Schuurmans, F. Southey. "Local Search Characteristics of Incomplete SAT Procedures". National Conference on Artificial Intelligence (AAAI), June 2000. view
116. D. Schuurmans. "Greedy Importance Sampling". Neural Information Processing Systems (NIPS), Denver, CO, USA, December 1999. PDFview
117. D. Schuurmans, L. Greenwald. "Efficient Exploration for Optimizing Immediate Reward". National Conference on Artificial Intelligence (AAAI), Orlando, Florida, July 1999. PDFview
118. A. Grove, D. Schuurmans. "Boosting in the limit: Maximizing the margin of learned ensembles". National Conference on Artificial Intelligence (AAAI), June 1998. PDFview
119. R. Greiner, A. Grove, D. Schuurmans. "Learning Bayesian Nets that Perform Well". Conference on Uncertainty in Artificial Intelligence (UAI), Providence, Rhode Island, August 1997. PDFview
120. A. Grove, N. Littlestone, D. Schuurmans. "General Convergence Results for Linear Discriminant Updates". Conference on Learning Theory (COLT), June 1997. view
121. D. Schuurmans. "A New Metric-Based Approach to Model Selection". National Conference on Artificial Intelligence (AAAI), Providence, Rhode Island, January 1997. PDFview
122. D. Schuurmans, L. Ungar, D. Foster. "Characterizing the Generalization Performance of Model Selection Strategies". International Conference on Machine Learning (ICML), Nashville, January 1997. PDFview
123. R. Greiner, D. Schuurmans. "Learning to Classify Incomplete Examples". Conference on Learning Theory (COLT), August 1996. PSview
124. D. Schuurmans, R. Greiner. "Practical PAC Learning". International Joint Conference on Artificial Intelligence (IJCAI), August 1995. PDFview
125. D. Schuurmans, R. Greiner. "Sequential PAC Learning". Conference on Learning Theory (COLT), Santa Cruz, California, pp 277-284, July 1995. PDFview
126. D. Schuurmans. "Characterizing Rational Versus Exponential Learning Curves". EuroCOLT, June 1995. view
127. D. Schuurmans, R. Greiner. "Learning Default Concepts". Canadian Conference on Artificial Intelligence (CAI), Banff, Canada, May 1994. PSview
128. R. Greiner, D. Schuurmans. "Learning Useful Horn Approximations". Knowledge Representation and Reasoning (KR), Cambridge, United States, October 1992. PSview
129. D. Schuurmans, J. Schaeffer. "Some diffculties with classifer representations". International Conference on Genetic Algorthms, June 1989. view
130. D. Schuurmans. "Learning with classifier systems". Canadian Information Processing Society, June 1987. view

In Conference (unrefereed)

131. Y. Guo, D. Schuurmans. "Efficient global optimization for exponential family PCA and low-rank matrix factorization". Allerton Conference on Communication, Control, and Computing, September 2008. view

In Workshop

132. Y. Li, L. Cheng, D. Schuurmans. "Inference of the structural credit risk model using MLE". IEEE Symposium on Computational Intelligence for Financial Engineering, February 2009. view
133. Y. Li, D. Schuurmans. "Policy iteration for learning an exercise policy for American options". European Workshop on Reinforcement Learning, July 2008. view
134. Y. Li, D. Schuurmans. "Learning exercise policies for American options". International Symposium on Financial Engineering, February 2008. view
135. Y. Guo, D. Schuurmans. "Learning Gene Regulatory Networks via Globally Regularized Risk Minimization". RECOMB Satellite Workshop on Comparative Genomics, pp 83-95, September 2007. view
136. F. Peng, X. Huang, D. Schuurmans, S. Wang. "Text classification in Asian languages without word segmentation". International Workshop on Information Retrieval with Asian Languages, June 2007. PDFview
137. S. Wang, R. Greiner, D. Schuurmans, L. Cheng, S. Wang. "Integrating Trigram, PCFG and LDA for Language Modeling via Directed Markov Random Fields". NIPS Workshop on Bayesian Methods for Natural Language Processing, December 2005. view
138. L. Xu, L. Cheng, T. Wang, D. Schuurmans. "Convex hidden Markov models". Workshop on Advances in Structured Learning for Text and Speech Processing (within NIPS), January 2005. view
139. L. Cheng, S. Wang, D. Schuurmans, T. Caelli. "On-line learning with sparse kernels for video analysis". Workshop on Large-Scale Kernel Machines (within NIPS), January 2005. view
140. D. Schuurmans, F. Southey. "Local Search Characteristics of Incomplete SAT Procedures". Value of Information in Inference, Learning and Decision-Making, July 2002. PDFview
141. R. Greiner, D. Schuurmans. "Learning an Optimally Accurate Representational System". ECAI Workshop on Theoretical Foundations of Knowledge Representation and Reasoning, Springer Verlag, August 1993. PSview
142. W. Cohen, R. Greiner, D. Schuurmans. "Probabilistic Hill-Climbing". Computational Learning Theory and Natural Learning Systems, (Edition II), MIT Press, pp 171--181, January 1992. view

Other Categories

143. M. Elgendi, I. Norton, M. Brearley, D. Abbott, D. Schuurmans. "Systolic Peak Detection in Acceleration Photoplethysmograms Measured from Emergency Responders in Tropical Conditions". In Magazine, PLoS One, (ed: Vladimir E. Bondarenko), 8(10), February 2013. view
144. D. Schuurmans, F. Southey, D. Wilkinson, Y. Guo. "Metric-based approaches for semisupervised". MIT Press, June 2007. view
145. D. Schuurmans, F. Southey, D. Wilkinson, Y. Guo. "Metric-based approaches for semi-supervised regression and classification". Semi-Supervised Learning, MIT Press, (ed: O. Chapelle, B. Schoelkopf, A. Zein), January 2006. PDFview
146. A. Smola, P. Bartlett, B. Scholkopf, D. Schuurmans. "Introduction to Large Margin Classifiers". Value of Information in Inference, Learning and Decision-Making, Whistler, B.C., Canada, December 2005. view
147. Q. Wang, D. Schuurmans, D. Lin. "Strictly lexical dependency parsing". International Workshop on Parsing Technologies (IWPT), January 2005. PDFview
148. Y. Guo, D. Schuurmans. "Support Vector Machines on General Confidence Functions". January 2005. view
149. L. Cheng, B. Bai, C. Lei, D. Schuurmans, S. Wang. "Shape Time Discriminative Classification of Video Objects". 2005. view
150. R. Greiner, D. Schuurmans. "ICML 2004 Conference Proceedings". International Conference on Machine Learning (ICML), July 2004. view
151. R. Greiner, D. Schuurmans, C. O'Brien. "Efficient estimation exploiting independence constraints". January 2002. PSview
152. R. Greiner, D. Schuurmans. "Fast Distribution-Specific Learning". Computational Learning Theory and Natural Learning Systems, MIT Press, 4, pp 155-167, August 1997. view
153. D. Schuurmans, R. Greiner. "Learning to Classify Incomplete Examples". Computational Learning Theory and Natural Learning Systems, MIT Press, 4, pp 87-105, May 1997. PDFview
154. D. Schuurmans. "Effective Classification Learning". Value of Information in Inference, Learning and Decision-Making, January 1996. view
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