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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
13. | F. Peng, D. Schuurmans, S. Wang. "Augmenting Naive Bayes Classifiers with Statistical Language Models". Information Retrieval (IR), September 2003. |
14. | A. Ghodsi, D. Schuurmans. "Automatic basis selection techniques for RBF networks". Neural Networks, 16(5-6), pp 809-816, June 2003. |
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. |
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. |
17. | D. Schuurmans, F. Southey. "Local search characteristics of incomplete SAT procedures". Artificial Intelligence (AIJ), 132(2), pp 121--150, June 2001. |
18. | D. Schuurmans. "Characterizing Rational Versus Exponential Learning Curves". Journal of Computer System Sciences, 55(1), pp 140-160, March 1997. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
32. | F. Mirzazadeh, Y. Guo, D. Schuurmans. "Convex co-embedding". National Conference on Artificial Intelligence (AAAI), pp 1989-1996, July 2014. |
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. |
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. |
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. |
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. |
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. |
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. |
39. | Y. Guo, D. Schuurmans. "Multi-label Classification with Output Kernels". European Conference on Machine Learning (ECML), pp 417-432, September 2013. |
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. |
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. |
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. |
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. |
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. |
45. | Y. Yu, Y. Li, C. Szepesvari, D. Schuurmans. "A general projection property for distribution families". Neural Information Processing Systems (NIPS), December 2009. |
46. | N. Quadrianto, T. Caetano, J. Lim, D. Schuurmans. "Convex relaxation of mixture regression with efficient algorithms". Neural Information Processing Systems (NIPS), December 2009. |
47. | Y. Guo, D. Schuurmans. "A reformulation of support vector machines for general confidence functions". Asian Conference on Machine Learning, November 2009. |
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. |
49. | L. Xu, W. Li, D. Schuurmans. "Fast normalized cut with linear constraints". Computer Vision and Pattern Recognition (CVPR), June 2009. |
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. |
51. | M. Yang, Y. Li, D. Schuurmans. "Dual temporal difference learning". Artificial Intelligence and Statistics, April 2009. |
52. | Y. Li, C. Szepesvari, D. Schuurmans. "Learning exercise policies for American options". Artificial Intelligence and Statistics, April 2009. |
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. |
54. | Y. Guo, D. Schuurmans. "Convex relaxations of latent variable training". Neural Information Processing Systems (NIPS), December 2007. |
55. | Y. Guo, D. Schuurmans. "Discriminative batch mode active learning". Neural Information Processing Systems (NIPS), December 2007. |
56. | T. Wang, D. Lizotte, M. Bowling, D. Schuurmans. "Stable dual dynamic programming". Neural Information Processing Systems (NIPS), December 2007. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
65. | L. Cheng, S. Wang, D. Schuurmans, T. Caelli, S. Vishwantathan. "An online discriminative approach to background subtraction". IEEE, January 2006. |
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. |
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. |
68. | L. Xu, D. Wilkinson, F. Southey, D. Schuurmans. "Discriminative Unsupervised Learning of Structured Predictors". International Conference on Machine Learning (ICML), Pittsburgh, January 2006. |
69. | L. Cheng, S. Vishwantathan, D. Schuurmans, S. Wang, T. Caelli. "Implicit Online Learning with Kernels". Neural Information Processing Systems (NIPS), January 2006. |
70. | F. Jiao, J. Xu, L. Yu, D. Schuurmans. "Protein fold recognition using the gradient boost algorithm". Computational Systems Bioinformatics Conference (CSB), January 2006. |
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. |
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. |
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. |
74. | Y. Guo, R. Greiner, D. Schuurmans. "Learning Coordination Classifiers". International Joint Conference on Artificial Intelligence (IJCAI), Edinburgh, Scotland, August 2005. |
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. |
76. | A. Ghodsi, J. Huang, F. Southey, D. Schuurmans. "Tangent Corrected Embedding". Computer Vision and Pattern Recognition (CVPR), June 2005. |
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. |
78. | Q. Wang, D. Schuurmans. "Improved estimation for unsupervised part-of-speech tagging". IEEE, January 2005. |
79. | Y. Guo, D. Wilkinson, D. Schuurmans. "Maximum Margin Bayesian Networks". Conference on Uncertainty in Artificial Intelligence (UAI), Edinburgh, Scotland, January 2005. |
80. | L. Xu, D. Schuurmans. "Unsupervised and Semi-Supervised Multi-Class Support Vector Machines". National Conference on Artificial Intelligence (AAAI), Pittsburgh, January 2005. |
81. | L. Cheng, F. Jiao, D. Schuurmans, S. Wang. "Variational Bayesian Image Modelling". International Conference on Machine Learning (ICML), Bonn, Germany, January 2005. |
82. | A. Ghodsi, J. Huang, D. Schuurmans. "Transformation-Invariant Embedding for image analysis". European Conference on Computer Vision (ECCV), Prague, Czech Republic, May 2004. |
83. | L. Xu, J. Neufeld, B. Larson, D. Schuurmans. "Maximum Margin Clustering". Neural Information Processing Systems (NIPS), Vancouver, British Columbia, Canada, January 2004. |
84. | S. Wang, D. Schuurmans. "Learning Continuous Latent Variable Models With Bregman Divergences". ICASSP, October 2003. |
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. |
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. |
87. | A. Ghodsi, D. Schuurmans. "Automatic Basis Selectsion for RBF Networks". IJCNN, July 2003. |
88. | A. Ghodsi, D. Schuurmans. "Automatic basis selection for RBF networks using Stein's unbiased risk estimator". IJCNN, June 2003. |
89. | A. Ghodsi, D. Schuurmans. "Automatic Complexity Control for System Identification". Fuzzy Systems Association World Congress(IFSA), June 2003. |
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. |
91. | F. Lu, D. Schuurmans. "Model-Based Least-Squares Policy Evalulation". Canadian Conference on Artificial Intelligence (CAI), Halifax, Nova Scotia, Canada, June 2003. |
92. | F. Peng, D. Schuurmans, S. Wang. "Language and Task Independent Text Categorization With Simple Language Models". HLT-NAACL, May 2003. |
93. | F. Peng, D. Schuurmans, S. Wang. "Language independent authorship attribution using character level language models". EACL, April 2003. |
94. | S. Wang, D. Schuurmans, F. Peng, Y. Zhao. "Semantic N-Gram Language Modelling With the Latent Maximum Entropy Principle". ICASSP, April 2003. |
95. | F. Peng, D. Schuurmans. "Combining Naive Bayes and n-Gram Language Models for Text Classification". ECIR, January 2003. |
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. |
97. | F. Lu, D. Schuurmans. "Monte Carlo matrix inversion policy evaluation". Conference on Uncertainty in Artificial Intelligence (UAI), Acapulco, Mexico, January 2003. |
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. |
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. |
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. |
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. |
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. |
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. |
104. | S. Wang, R. Rosenfeld, Y. Zhao, D. Schuurmans. "The latent maximum entropy principle". International Symposium on Information Theory (ISIT), June 2002. |
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. |
106. | F. Southey, D. Schuurmans, A. Ghodsi. "Regularized Greedy Importance Sampling". Neural Information Processing Systems (NIPS), Vancouver, British Columbia, Canada, January 2002. |
107. | F. Peng, X. Huang, D. Schuurmans, N. Cercone, S. Robertson. "Using Self-Supervised Word Segmentation in Chinese Information Retrieval". SIGIR, January 2002. |
108. | F. Peng, D. Schuurmans. "A Simple Closed-Class/Open-Class Factorization for Improved Language Modeling". Natural Language Processing Pacific Rim Symposium, December 2001. |
109. | D. Schuurmans, R. Patrascu. "Direct Value-Approximation for Factored MDPs". Neural Information Processing Systems (NIPS), Vancouver, British Columbia, Canada, December 2001. |
110. | F. Peng, D. Schuurmans. "A Hierarchical EM Approach to Word Segmentation". Natural Language Processing Pacific Rim Symposium, November 2001. |
111. | F. Peng, D. Schuurmans. "Self-Supervised Chinese Word Segmentation". International Joint Conference on Artificial Intelligence (IJCAI), September 2001. |
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. |
113. | D. Schuurmans, A. Bistritz, F. Southey. "Monte Carlo Inference Via Greedy Importance Sampling". Conference on Uncertainty in Artificial Intelligence (UAI), July 2000. |
114. | D. Schuurmans, F. Southey. "An adaptive regularization criterion for supervised learning". International Conference on Machine Learning (ICML), Stanford University, June 2000. |
115. | D. Schuurmans, F. Southey. "Local Search Characteristics of Incomplete SAT Procedures". National Conference on Artificial Intelligence (AAAI), June 2000. |
116. | D. Schuurmans. "Greedy Importance Sampling". Neural Information Processing Systems (NIPS), Denver, CO, USA, December 1999. |
117. | D. Schuurmans, L. Greenwald. "Efficient Exploration for Optimizing Immediate Reward". National Conference on Artificial Intelligence (AAAI), Orlando, Florida, July 1999. |
118. | A. Grove, D. Schuurmans. "Boosting in the limit: Maximizing the margin of learned ensembles". National Conference on Artificial Intelligence (AAAI), June 1998. |
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. |
120. | A. Grove, N. Littlestone, D. Schuurmans. "General Convergence Results for Linear Discriminant Updates". Conference on Learning Theory (COLT), June 1997. |
121. | D. Schuurmans. "A New Metric-Based Approach to Model Selection". National Conference on Artificial Intelligence (AAAI), Providence, Rhode Island, January 1997. |
122. | D. Schuurmans, L. Ungar, D. Foster. "Characterizing the Generalization Performance of Model Selection Strategies". International Conference on Machine Learning (ICML), Nashville, January 1997. |
123. | R. Greiner, D. Schuurmans. "Learning to Classify Incomplete Examples". Conference on Learning Theory (COLT), August 1996. |
124. | D. Schuurmans, R. Greiner. "Practical PAC Learning". International Joint Conference on Artificial Intelligence (IJCAI), August 1995. |
125. | D. Schuurmans, R. Greiner. "Sequential PAC Learning". Conference on Learning Theory (COLT), Santa Cruz, California, pp 277-284, July 1995. |
126. | D. Schuurmans. "Characterizing Rational Versus Exponential Learning Curves". EuroCOLT, June 1995. |
127. | D. Schuurmans, R. Greiner. "Learning Default Concepts". Canadian Conference on Artificial Intelligence (CAI), Banff, Canada, May 1994. |
128. | R. Greiner, D. Schuurmans. "Learning Useful Horn Approximations". Knowledge Representation and Reasoning (KR), Cambridge, United States, October 1992. |
129. | D. Schuurmans, J. Schaeffer. "Some diffculties with classifer representations". International Conference on Genetic Algorthms, June 1989. |
130. | D. Schuurmans. "Learning with classifier systems". Canadian Information Processing Society, June 1987. |
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. |
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. |
133. | Y. Li, D. Schuurmans. "Policy iteration for learning an exercise policy for American options". European Workshop on Reinforcement Learning, July 2008. |
134. | Y. Li, D. Schuurmans. "Learning exercise policies for American options". International Symposium on Financial Engineering, February 2008. |
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. |
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. |
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. |
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. |
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. |
140. | D. Schuurmans, F. Southey. "Local Search Characteristics of Incomplete SAT Procedures". Value of Information in Inference, Learning and Decision-Making, July 2002. |
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. |
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. |
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. |
144. | D. Schuurmans, F. Southey, D. Wilkinson, Y. Guo. "Metric-based approaches for semisupervised". MIT Press, June 2007. |
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. |
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. |
147. | Q. Wang, D. Schuurmans, D. Lin. "Strictly lexical dependency parsing". International Workshop on Parsing Technologies (IWPT), January 2005. |
148. | Y. Guo, D. Schuurmans. "Support Vector Machines on General Confidence Functions". January 2005. |
149. | L. Cheng, B. Bai, C. Lei, D. Schuurmans, S. Wang. "Shape Time Discriminative Classification of Video Objects". 2005. |
150. | R. Greiner, D. Schuurmans. "ICML 2004 Conference Proceedings". International Conference on Machine Learning (ICML), July 2004. |
151. | R. Greiner, D. Schuurmans, C. O'Brien. "Efficient estimation exploiting independence constraints". January 2002. |
152. | R. Greiner, D. Schuurmans. "Fast Distribution-Specific Learning". Computational Learning Theory and Natural Learning Systems, MIT Press, 4, pp 155-167, August 1997. |
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. |
154. | D. Schuurmans. "Effective Classification Learning". Value of Information in Inference, Learning and Decision-Making, January 1996. |