Books

• Dietterich, T. G., Becker, S., and Ghahramani, Z. (Eds.) (2002) Advances in Neural Information Processing Systems, 14, Cambridge, MA: MIT Press.
• Leen, T. K., Dietterich, T. G., and Tresp, V. (2001) Advances in Neural Information Processing Systems, 13, Cambridge, MA: MIT Press.
• Shavlik, J., and Dietterich, T. G., (1990) Readings in Machine Learning. San Mateo, CA: Morgan Kaufmann.
  

Journal Papers

• Langford, W. T., Gergel, S. E., Dietterich, T. G., Cohen, W. Map misclassification can cause large errors in landscape pattern indices: Examples from habitat fragmentation. Ecosystems, 9 (3), pp. 474-488, 2006.
• Bayer-Zubek, V., Dietterich, T. G. Integrating Learning from Examples into the Search for Diagnostic Policies. Journal of Artificial Intelligence Research, 24, 263-303, 2005.
• Valentini, G., Dietterich, T. G., "Bias-variance analysis of Support Vector Machines for the development of SVM-based ensemble methods," accepted for publication Journal of Machine Learning Research, 2004.
• T.G. Dietterich, "Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition," Journal of Artificial Intelligence Research, 13, pp. 227-303, 2000.
• X. Wang and T. G. Dietterich, "Efficient Value Function Approximation Using Regression Trees," pages 51-54 of collective article: J. Boyan, W. Buntine, and A. Jagota (Eds.), "Statistical Machine Learning for Large Scale Optimization," Neural Computing Surveys, 3, pp. 1-58, 2000.
• T. G. Dietterich, "An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization," Machine Learning, 40(2), pp. 139-158, 2000.
• T. G. Dietterich, "Statistical Tests for Comparing Supervised Classification Learning Algorithms," Neural Computation, 10(7), 1895-1924, 1998.
  

Conference Proceedings

• Larios, N., Deng, H., Zhang, W., Sarpola, M., Yuen, J., Paasch, R., Moldenke, A., Lytle, D., Ruiz Correa, S., Mortensen, E., Shapiro, L. G., Dietterich T. G. “Automated Insect Identification through Concatenated Histograms of Local Appearance Features. To appear in IEEE Workshop on Applications of Computer Vision (WACV-2007), Austin, TX. 2007.
• Stumpf, S., Rajaram, V., Li, L., Burnett, M., Dietterich, T., Sullivan, E., Drummond, R., Herlocker, J. “Toward harnessing user feedback for machine learning.” To appear in International Conference on Intelligent User Interfaces (IUI-2007), Honolulu, HI, 2007.
• Shen, J., Dietterich, T. “Active EM to reduce noise in activity recognition.” To appear in International Conference on Intelligent User Interfaces (IUI-2007), Honolulu, HI, 2007.
• Shen, J., Li, L., Dietterich, T. “Real-time detection of task switches of desktop users.” To appear in International Joint Conference on Artificial Intelligence (IJCAI-07), Hyderabad, India, 2007.
• Bao, X., Herlocker, J., Dietterich, T. “Fewer clicks and less frustration: Reducing the cost of reaching the right folder.” International Conference on Intelligent User Interfaces (IUI-2006). Sydney, Australia. 2006.
• Shen, J., Li, L., Dietterich, T., Herlocker, J. “A Hybrid Learning System for Recognizing User Tasks from Desktop Activities and Email Messages.” International Conference on Intelligent User Interfaces (IUI-2006). Sydney, Australia, 2006.
• Dragunov, A. N., Dietterich, T. G., Johnsrude, K., McLaughlin, M., Li, L., Herlocker, J. L. TaskTracer: “A Desktop Environment to Support Multi-tasking Knowledge Workers.” International Conference on Intelligent User Interfaces (IUI-2005), San Diego, 2005.
• Natarajan, S., Tadepalli, P., Altendorf, E., Dietterich, T. G., Fern, A., Restificar, A. (2005). “Learning first-order probabilistic models with combining rules.” Proceedings of the 22nd International Conference on Machine Learning (ICML-2005). Bonn, Germany. 2005.
• Altendorf, E., Restificar, E., Dietterich, T. G. “Learning from sparse data by exploiting monotonicity constraints.” Proceedings of the 21st Conference on Uncertainty in Artificial Intelligence (UAI-05), Edinburgh, Scotland, 2005.
• Dietterich, T. G., Ashenfelter, A., Bulatov, Y., "Training conditional random fields via gradient tree boosting," to appear in Proceedings of the International Conference on Machine Learning (ICML-2004), 2004.
• Wu, P., Dietterich, T. G., "Improving SVM accuracy by training on auxiliary data sources," to appear in Proceedings of the International Conference on Machine Learning (ICML-2004), 2004.
  • Wang, X., Dietterich, T. G., "Model-based Policy Gradient Reinforcement Learning," Proceedings of the International Conference on Machine Learning (ICML-2003). Cambridge, MA, 2003.
• Valentini, G., Dietterich, T. G. (2003), "Low Bias Bagged Support Vector Machines," Proceedings of the International Conference on Machine Learning (ICML-2003), Cambridge, MA, 2003.
• Dietterich, T. G., Busquets, D., Lopez de Mantaras, R., Sierra, C., "Action Refinement in Reinforcement Learning by Probability Smoothing," Proceedings of the International Conference on Machine Learning (ICML-2002), pp. 107-114, San Francisco, 2002.
• Zubek, V. B., Dietterich, T. G., "Pruning Improves Heuristic Search for Cost-Sensitive Learning," Proceedings of the International Conference on Machine Learning (ICML-2002), pp. 27-34, San Francisco, 2002.
• Busquets, D., Lopez de Mantaras, R., Sierra, C., Dietterich, T. G., "Reinforcement Learning for Landmark-based Robot Navigation," Autonomous Agents and Multi-Agent Systems, pp. 841-842, ACM Press, 2002.
• TG Dietterich and X. Wang, "Batch Value Function Approximation via Support Vectors," Advances in Neural Information Processing Systems 14, TG Dietterich, S. Becker and Z. Ghahramani (eds.), Cambridge, MA, 2002.
• X. Wang and TG Dietterich, "Stabilizing Value Function Approximation with the BFBP Algorithm," Advances in Neural Information Processing Systems 14, TG Dietterich, S. Becker, and Z. Ghahramani (eds.), Cambridge, MA, 2002.
• D. Margineantu and TG Dietterich, "Lazy Class Probability Estimators," 33rd Symposium on the Interface of Computing Science and Statistics, Costa Mesa, CA, 2001.
• V.B. Zubek and TG Dietterich, "A POMDP Approximation Algorithm that Anticipates the Need to Observe," Proceedings of the Pacific Rim Conference on Artificial Intelligence (PRIACAI-2000); Lecture Notes in Computer Science, Springer-Verlag, pp. 521-532, 2000.
• T. Fountain, TG Dietterich, and B. Sudyka, "Mining IC Test Data to Optimize VLSI Testing," Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery& Data Mining, ACM Press, pp. 18-25, 2000. Winner, Best Application Paper (Research Track).
• D.D. Margineantu and TG Dietterich, "Bootstrap Methods for the Cost-Sensitive Evaluation of Classifiers," Proceedings of the Seventeenth International Conference on Machine Learning, pp. 582-590, San Francisco, 2000.
• E. Chown and TG Dietterich, "A Divide-and-Conquer Approach to Learning from Prior Knowledge," Proceedings of the Seventeenth International Conference on Machine Learning, pp. 143-150, San Francisco, 2000.