Publications by Thomas G. Dietterich

2023

Trimponias, G., Dietterich, T. G. (2023). Reinforcement learning with exogenous states and rewards. Arxiv preprint. Under review.

Wagstaff, K. L., Dietterich, T. G. (2023). Hidden heterogeneity: When to choose similarity-based calibration. Transactions on Machine Learning Research Open Review.

2022

Guyer, A., Dietterich, T. G. (2022). Will My Robot Achieve My Goals? Predicting the Probability that an MDP Policy Reaches a User-Specified Behavior Target. Arxiv preprint. Note: The original version contained an error. We have uploaded a corrected revision.

Wagstaff, K. L., Dietterich, T. G. (2022). Hidden heterogeneity: When to choose similarity-based calibration. Arxiv preprint.

Dietterich, T. G., Hostetler, J. (2022). Conformal prediction intervals for Markov Decision Process trajectories. Arxiv preprint. Under review.

Dietterich, T. G., Guyer, A. (2022). The familiarity hypothesis: Explaining the behavior of deep open set methods. Pattern Recognition, 132. p. 108931. Arxiv preprint.

Liu, S., Garrepallli, R., Hendrycks, D., Fern, A., Mondal, D., Dietterich, T. G. (2022). PAC Guarantees and Effective Algorithms for Detecting Novel Categories. Journal of Machine Learning Research, 23(44): 1-47. JMLR page

2021

Lukas Ruff, Jacob R. Kauffmann, Robert A. Vandermeulen, Gregoire Montavon, Wojciech Samek, Marius Kloft, Thomas G. Dietterich, Klaus-Robert Mueller (2021). A Unifying Review of Deep and Shallow Anomaly Detection. Proc. IEEE, 109 (5): 756-795. arXiv version

Jonathan Ferrer-Mestres, Thomas G. Dietterich, Olivier Buffet, Iadine Chades (2021). K-N-MOMDPs: Towards Interpretable Solutions for Adaptive Management. AAAI 2021, 14775-14784. PDF.

Yunye Gong, Xiao Lin, Yi Yao, Thomas G. Dietterich, Ajay Divakaran, Melinda T. Gervasio. (2021). Confidence Calibration for Domain Generalization under Covariate Shift. ICCV 2021: 8938-8947. IEEE Explore.

Shiv Shankar, Daniel Sheldon, Tao Sun, John Pickering, Thomas G. Dietterich. (2021). Three-quarter Sibling Regression for Denoising Observational Data. Arxiv preprint.

Erich Merrill, Stefan Lee, Fuxin Li, Thomas G. Dietterich, Alan Fern (2021). Deep Convolution for Irregularly Sampled Temporal Point Clouds. Arxiv preprint.

2020

Alspector, J., Dietterich, T. G. (2020). DARPA's role in machine learning. AI Magazine, 41 (2): 36-48. Online version.

Das, S., Wong, W-K., Dietterich, T. G., Fern, A., Emmott, A. (2020). Discovering Anomalies by Incorporating Feedback from an Expert. ACM Trans. Knowl. Discov. Data, 14(4): 49:1-49:32.

Ferrer-Mestres, J., Dietterich, T. G., Buffet, O., Chades, I. (2020). Solving K-MDPs. ICAPS 2020. 110-118.

Zemicheal, T., Dietterich, T. G. (2020). Conditional Mixture Models for Precipitation Data Quality Control. Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS-2020). PDF.

Lauer, C. J., Montgomery, C. A., Dietterich, T. G. (2020). Evaluating wildland fire liability standards---does regulation incentivise good management? International Journal of Wildland Fire. DOI.

2019

Carla P. Gomes, Thomas G. Dietterich, Christopher Barrett, Jon Conrad, Bistra Dilkina, Stefano Ermon, Fei Fang, Andrew Farnsworth, Alan Fern, Xiaoli Z. Fern, Daniel Fink, Douglas H. Fisher, Alexander Flecker, Daniel Freund, Angela Fuller, John M. Gregoire, John E. Hopcroft, Steve Kelling, J. Zico Kolter, Warren B. Powell, Nicole D. Sintov, John S. Selker, Bart Selman, Daniel Sheldon, David B. Shmoys, Milind Tambe, Weng-Keen Wong, Christopher Wood, Xiaojian Wu, Yexiang Xue, Amulya Yadav, Abdul-Aziz Yakubu, Mary Lou Zeeman. (2019). Computational sustainability: computing for a better world and a sustainable future. Communications of the ACM, 62 (9): 56-65. CACM Open Access Version.

Zemicheal, T., Dietterich, T. G. (2019) Anomaly Detection in the Presence of Missing Values for Weather Data Quality Control. Proceedings of the 2nd ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS-2019). 65-73. Accra, Ghana. PDF Preprint.

Siddiqui, M. A., Fern, A., Dietterich, T. G., Wong, W-K. (2019). Sequential Feature Explanations for Anomaly Detection. ACM Transactions on Knowledge Discovery from Data, 13(1): Article No. 1. HTML Version at ACM. PDF preprint.

Shankar, S., Sheldon, D., Sun, T., Pickering, J., Dietterich, T. G. (2019). Three-quarter Sibling Regression for Denoising Observational Data. Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, pp. 5960-5966. Published version. PDF Preprint.

Lauer, C. J., Montgomery, C. A., Dietterich, T. G. (2019). Managing Fragmented Fire-Threatened Landscapes With Spatial Externalities. Forest Science. Publisher's version.

Hendrycks, D., Dietterich, T. G. (2019). Benchmarking Neural Network Robustness to Common Corruptions and Perturbations.. International Conference on Learning Representations (ICLR 2019) PDF at OpenReview arXiv PDF

Hendrycks, D., Mazeika, M., Dietterich, T. G. (2019) Deep Anomaly Detection with Outlier Exposure. International Conference on Learning Representations (ICLR 2019) PDF at Open Review arXiv PDF

Dietterich, T. G. (2019). Robust Artificial Intelligence and Robust Human Organizations. Frontiers of Computer Science, 13(1): 1-3. SharedIt Link to Publisher's Page DOI 10.1007/s11704-018-8900-4.

Albers, H. J., Dietterich, T. G., Hall, K. M., Lee, K. D., Taleghan, M. A. (2019). Simulator-Defined Markov Decision Processes: A Case Study in Managing Bio-invasions. In Fei Fang, Milind Tambe, Andrew Plumptre, Bistra Dilkina (Eds.) Artificial Intelligence and Conservation. Cambridge University Press. DOI

McGregor, S., Houtman, R., Metoyer, R., Dietterich, T. G. (2019). Connecting Conservation Research and Implementation: Building a Wildfire Assistant. In Fei Fang, Milind Tambe, Andrew Plumptre, Bistra Dilkina (Eds.) Artificial Intelligence and Conservation. Cambridge University Press. DOI.

2018

Hendrycks, D., Dietterich, T. G. (2018). Benchmarking neural network robustness to common corruptions and surface variations. arXiv 1807.01697

Siddiqui, M. A., Fern, A., Dietterich, T., Wright, R., Theriault, A., Archer, D. W. (2018). Feedback-Guided Tree-Based Anomaly Detection via Online Optimization. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. PDF Preprint

Dietterich, T. G., Zemicheal, T. (2018). Anomaly Detection in the Presence of Missing Values. KDD ODD Workshop 2018. arXiv 1809.01605.

Albers, Heidi J., Hall, Kim Meyer, Lee, Katherine D.Lee, Alkaee Taleghan, M. Dietterich, Thomas G. (2018). The Role of Restoration and Key Ecological Invasion Mechanisms in Optimal Spatial-Dynamic Management of Invasive Species. Ecological Economics, 151 44--54. DOI 10.1016/j.ecolecon.2018.03.031

Hall, Kim Meyer, Albers, Heidi J., Alkaee Taleghan, Majid, Dietterich, Thomas G. (2018). Optimal Spatial-Dynamic Management of Stochastic Species Invasions. Environmental and Resource Economics, 70(2): 403-427. Published version.

Dietterich, T. G., Trimponias, G., Chen, Z. (2018). Discovering and Removing Exogenous State Variables and Rewards for Reinforcement Learning. Proceedings of the 35th International Conference on Machine Learning (ICML 2018), Proceedings of Machine Learning Research, 80, 1261-1269

Liu, S., Garrepalli, R., Dietterich, T. G., Fern, A., Hendrycks, D. (2018). Open Category Detection with PAC Guarantees. Proceedings of the 35th International Conference on Machine Learning (ICML 2018). Proceedings of Machine Learning Research, 80, 3175-3184.

Alkaee-Taleghan, M., Dietterich, T. G. (2018). Efficient Exploration for Constrained MDPs. AAAI 2018 Spring Symposium. Published version

2017

Hostetler, J., Fern, A., Dietterich, T. G. (2017). Sample-based tree search with fixed and adaptive state abstractions. Journal of Artificial Intelligence Research, 60, 717-777. Published Version

McGregor, S., Houtman, R., Montgomery, C., Metoyer, R., Dietterich, T. G.. (2017) Factoring Exogenous State for Model-Free Monte Carlo. arXiv preprint 1703.09390

McGregor, S., Houtman, R., Montgomery, C., Metoyer, R., Dietterich, T. G. (2017). Fast Optimization of Wildfire Suppression Policies with SMAC. arXiv preprint arXiv:1703.09391

Dietterich, T. (2017). Steps Toward Robust Artificial Intelligence. AI Magazine, 38(3): 3-24. Read at AI Magazine. Video

Lauer, C. J., Montgomery, C. A., Dietterich, T. G. (2017). Spatial interactions and optimal forest management on a fire-threatened landscape. Forest Policy and Economics, 83: 107-120. Read at ScienceDirect. DOI.

O'Leary, M. A., Alphonse, K., Arce, M., Cavaliere, D., Cirranello, A., Dietterich, T., Julius, M., Kaufman, S., Law, E., Passarotti, M., Reft, A., Robalino, J., Simmons, N. B., Smith, S., Stevenson, D., Theriot, E., Velazco, P. M., Walls, R., Yu, Mengjie, Daly, Marymegan. (2017). Crowds Replicate Performance of Scientific Experts Scoring Phylogenetic Matrices of Phenotypes. Systematic Biology, 67(1), 49-60. https://doi.org/10.1093/sysbio/syx052

McGregor, S., Buckingham, H., Dietterich, T. G., Houtman, R., Montgomery, C., Metoyer, R. (2017). Interactive Visualization for Testing Markov Decision Processes. Journal of Visual Languages and Computing, 39. 93-106. PDF Preprint.

Hall, K. M., Albers, H. J., Taleghan, M. A., Dietterich, T. G. (2017). Optimal Spatial-Dynamic Management of Stochastic Species Invasions, Environmental and Resource Economics, 1-25. DOI. Read online at SharedIt.

McGregor, S., Buckingham, H., Dietterich, T. G., Houtman, R., Montgomery, C., Metoyer, R. (2017). Facilitating Testing and Debugging of Markov Decision Processes with Interactive Visualization: MDPvis. Journal of Visual Languages and Computing. PDF Preprint. DOI

Dujardin, Yann, Dietterich, Thomas G., Chades, Iadine (2017). Three new algorithms to solve N-POMDPs. AAAI Conference on Artificial Intelligence, AAAI-2017. PDF.

2016

Shubhomoy Das, Weng-Keen Wong, Thomas Dietterich, Alan Fern, Andrew Emmott (2016). Incorporating Expert Feedback into Active Anomaly Discovery. IEEE International Conference on Data Mining (ICDM-2016), Barcelona, Spain. PDF

Liping Liu, Thomas G. Dietterich, Nan Li, Zhi-Hua Zhou (2016). Transductive Optimization of Top k Precision. International Joint Conference on Artificial Intelligence (IJCAI-2016). pp. 1781-1787. New York, NY. PDF.

Md. Amran Siddiqui, Alan Fern, Thomas G. Dietterich, Shubhomoy Das (2016). Finite Sample Complexity of Rare Pattern Anomaly Detection. Uncertainty in Artificial Intelligence (UAI-2016). New York, NY. PDF.

Alkaee-Taleghan, M., Hall, K., Crowley, M., Albers, H. J., Dietterich, T. G. (2015). PAC Optimal MDP Planning for Ecosystem Management. Journal of Machine Learning Research, 16, 3877-3903. PDF version.

Md Amran Siddiqui, Alan Fern, Thomas Dietterich, Weng-Keen Wong. (2015). Sequential Feature Explanations for Anomaly Detection. SIGKDD Workshop on Outlier Detection and Description.. PDF Preprint.

Mohammed Shahed Sorower, Michael Slater, Thomas Dietterich (2015). Improving Automated Email Tagging with Implicit Feedback. UIST '15 Proceedings of the 28th Annual ACM Symposium on User Interface Software and Technology. 201-211 DOI 10.1145/2807442.2807501. PDF Preprint. Presentation.

Sean McGregor, Hailey Buckingham, Thomas G. Dietterich, Rachel Houtman, Claire Montgomery, and Ronald Metoyer (2015). Facilitating Testing and Debugging of Markov Decision Processes with Interactive Visualization. Proceedings of the IEEE Symposium on Visual Languages and Human-Centric Computing (VL-HCC 2015). PDF Preprint.

Thomas Dietterich, Eric Horvitz (2015). Viewpoint: Rise of Concerns about AI: Reflections and Directions. Communications of the ACM, 58(10) 38-40. PDF version. Video.

Yann Dujardin, Thomas Dietterich, Iadine Chades (2015). alpha-min: A Compact Approximate Solver for Finite-Horizon POMDPs. International Joint Conference on Artificial Intelligence, IJCAI-2015. PDF.

Jesse Hostetler, Alan Fern, Thomas Dietterich (2015). Progressive Abstraction Refinement for Sparse Sampling. Uncertainty in Artificial Intelligence, UAI-2015. Amsterdam. PDF.

Michael Lam, Janardhan Rao Doppa, Sinisa Todorovic, Thomas Dietterich (2015). HC-Search for Structured Prediction in Computer Vision. IEEE Computer Vision and Pattern Recognition (CVPR-2015). PDF preprint.

Hutchinson, R. A., Valente, J. J., Emerson, S. C., Betts, M. G., Dietterich, T. G. (2015). Penalized likelihood methods improve parameter estimates in occupancy models. Methods in Ecology and Environment. Open Access Version.

Jun Xie, Chao Ma, Janardhan Rao Doppa, Prashanth Mannem, Xiaoli Fern, Thomas Dietterich, Prasad Tadepalli (2015). Learning Greedy Policies for the Easy-First Framework. AAAI Conference on Artificial Intelligence (AAAI-2015). PDF Preprint.

Judah, K., Fern, A., Dietterich, T., Tadepalli, P. (2014). Active Imitation Learning: Formal and Practical Reductions to I.I.D. Learning. Journal of Machine Learning Research, 15, 4105-4143. Published version.

Chao Ma, Janardhan Rao Doppa, Walker Orr, Prashanth Mannem, Xiaoli Fern, Tom Dietterich, Prasad Tadepalli (2014). Prune-and-Score: Learning for Greedy Coreference Resolution. Proceedings of International Conference on Empirical Methods in Natural Language Processing (EMNLP). October. PDF Preprint.

Farnsworth, A., Sheldon, D., Geevarghese, J., Irvine, J., Van Doren, B., Webb, K., Dietterich, T. G., Kelling, S. (2014). Reconstructing Velocities of Migrating Birds from Weather Radar--A Case Study in Computational Sustainability. AI Magazine, Summer, 31-48. DOI 10.1609/aimag.v35i2.2527.

Orr, J. W., Chambers, N., Doppa, J. R., Tadepalli, P., Fern, X., Dietterich, T. G. (2014). Learning Scripts as Hidden Markov Models. In Proceedings of the AAAI Conference on Artificial Intelligence. AAAI Press. PDF preprint

Hostetler, J., Fern, A., Dietterich, T. G. (2014). State Aggregation in Monte Carlo Tree Search. In Proceedings of the AAAI Conference on Artificial Intelligence. AAAI Press. PDF Preprint.

Liu, L-P., Sheldon, D., Dietterich, T. G. (2014). Gaussian Approximation of Collective Graphical Models. In Proceedings of the 2014 International Conference on Machine Learning (ICML 2014). Journal of Machine Learning Research Workshop and Conference Proceedings, 32 (1):1602-1610. PDF Preprint.

Liu, L-P., Dietterich, T. G. (2014). Learnability of the Superset Label Learning Problem. In Proceedings of the 2014 International Conference on Machine Learning (ICML 2014). Journal of Machine Learning Research Workshop and Conference Proceedings, 32 (1): 16290-1637. PDF Preprint.

Sullivan, B. L., Aycrigg, J. L., Barry, J. H., Bonney, R. E., Bruns, N., Cooper, C. B., Damoulas, T., Dhondt, A. A., Dietterich, T. G., Farnsworth, A., Fink, D., Fitzpatrick, J. W., Fredericks, T., Gerbracht, J., Gomes, C., Hochachka, W. M., Iliff, M. J., Lagoze, C., La Sorte, F. A., Merrifield, M., Morris, W., Hochachka, W. M., Iliff, M., Lagoze, C., La Sorte, F. A., Merrifield, M., Morris, W., Phillips, T. B., Reynolds, M., Rodewald, A. D., Rosenberg, K. V., Trautmann, N. M., Wiggins, A., Winkler, D. W., Wong, W.-K., Wood, C. L., Yu, J., Kelling, S. (2014). The eBird enterprise: An integrated approach to development and application of citizen science. Biological Conservation, 169, 31-40. doi:10.1016/j.biocon.2013.11.003 Open Access Download.

Senator, T. E., Goldberg, H. G., Memory, A., Young, W. T., Rees, B., Pierce, R., Huang, D., Reardon, M., Bader, D. A., Chow, E. Essa, I., Jones, J., Bettadapura, V., Chau, D., Green, O., Kaya, O., Zakrzewska, A., Briscoe, E., Mappus IV, R. L., McColl, R., Weiss, L., Dietterich, T. G., Fern, A., Wong, W-K., Das, S., Emmott, A., Irvine, J., Corkill, D., Friedland, L., Gentzel, A., Jensen, D., Lee, J-Y, Koutra, D., Faloutsos, C. (2013). Detecting Insider Threats in a Real Corporate Database of Computer Usage Activity. In 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-2013) (pp. 1393-1401). PDF Preprint.

Emmott, A. F., Das, S., Dietterich, T. G., Fern, A., Wong, W.-K. (2013). Systematic construction of anomaly detection benchmarks from real data. ODD '13 Proceedings of the ACM SIGKDD Workshop on Outlier Detection and Description. (pp. 16-21). DOI PDF Preprint.

Wagstaff, K. L., Lanza, N. L., Thompson, D. R., Dietterich, T. G., Gilmore, M. S. (2013). Guiding scientific discovery with explanations using DEMUD. Proceedings of the Twenty-Seventh Conference on Artificial Intelligence, 2013.. PDF Preprint.

Rachel M. Houtman, Claire A. Montgomery, Aaron R. Gagnon, David E. Calkin, Thomas G. Dietterich, Sean McGregor, Mark Crowley (2013). Allowing a Wildfire to Burn: Estimating the Effect on Future Fire Suppression Costs. International Journal of Wildland Fire 22, 871--882. PDF Preprint. Published Version

Burleigh, G., Alphonse, K., Alverson, A. J., Bik, H. M., Blank, C., Cirranello, A. L., Cui, H., Daly, M., Dietterich, T. G., Gasparich, G., Irvine, J., Julius, M., Kaufman, S., Law, E., Liu, J., Moore, L., O'Leary, M. A., Passarotti, M., Ranade, S., Simmons, N. B., Stevenson, D. W., Thacker, R. W., Theriot, E. C., Todorovic, S., Velazco, P. M., Walls, R. L., Wolfe, J. M., Yu, M. (2013). Next-generation phenomics for the Tree of Life. PLOS Currents Tree of Life. DOI 10.1371/currents.tol.085c713acafc8711b2ff7010a4b03733. PDF Preprint

Dietterich, T., Taleghan, M., Crowley, M. (2013). PAC Optimal Planning for Invasive Species Management: Improved Exploration for Reinforcement Learning from Simulator-Defined MDPs. In Proceedings of the AAAI Conference on Artificial Intelligence, AAAI-2013. AAAI Press. PDF corrected after publication (corrected again on 16 April 2014).

Sheldon, D., Farnsworth, A., Irvine, J., Van Doren, B., Webb, K., Dietterich, T. G., Kelling, S. (2013). Approximate Bayesian Inference for Reconstructing Velocities of Migrating Birds from Weather Radar. In Proceedings of the AAAI Conference on Artificial Intelligence, AAAI-2013. AAAI Press. PDF Preprint.

Sheldon, D., Sun, T., Kumar, A., Dietterich, T. G. (2013). Approximate Inference in Collective Graphical Models. In Proceedings of the International Conference on Machine Learning, ICML-2013. PDF Preprint.

Yao, Q., Liu, Q., Dietterich, T. G., Todorovic, S., Lin, J., Diao, G., Yang, B., Tang, J. (2013). Segmentation of touching insects based on optical flow and NCuts. Biosystems Engineering, 114, 67-77. PDF Preprint.

Liu, L., Dietterich, T. G. (2012). A Conditional Multinomial Mixture Model for Superset Label Learning. Advances in Neural Information Processing Systems (NIPS-2012). NIPS Foundation. PDF.

Judah, K., Fern, A., Dietterich, T. (2012). Active Imitation Learning via Reduction to I.I.D. Active Learning. Uncertainty in Artificial Intelligence: Proceedings of the Twenty-Eighth Conference (2012). 428-437. AUAI Press, Corvallis, OR. PDF.

Hostetler, J., Dereszynski, E., Dietterich, T., Fern, A. (2012). Inferring strategies from limited reconnaissance in real-time strategy games. Uncertainty in Artificial Intelligence: Proceedings of the Twenty-Eighth Conference (2012). 367--76. AUAI Press, Corvallis, OR. PDF Preprint.

Zhang, X. S., Shrestha, B., Yoon, S., Kambhampati, S., DiBona, P., Guo, J. K., McFarlane, D., Hofmann, M. O., Whitebread, K., Appling, D. S., Whitaker, E. T., Trewhitt, E. B., Ding, L., Michaelis, J. R., McGuinness, D. L., Hendler, J. A., Doppa, J. R., Parker, C. Dietterich, T. G., Tadepalli, P., Wong, W-K., Green, D., Rebguns, A., Spears, D., Kuter, U., Levine, G., DeJong, G., MacTavish, R. L., Ontanon, S., Radhakrishnan, J., Ram, A., Mostafa, H., Zafar, H., Zhang, C., Corkill, D., Lesser, V., Song, Z. (2012) An Ensemble Architecture for Learning Complex Problem-Solving Techniques From Demonstration. ACM Transactions on Intelligent Systems and Technology, 3(4), 38 pp. PDF Preprint.

Dietterich, T. G., Dereszynski, E., Hutchinson, R. A., Sheldon, D. (2012). Machine Learning for Computational Sustainability. International Conference on Green Computing (IGCC-2012). Full open access version without IEEE copyright.

Sheldon, D., Dietterich, T. G. (2011). Collective Graphical Models. 2011 Conference on Neural Information Processing Systems (NIPS-2011). PDF Preprint.

Sorower, M. S., Dietterich, T. G., Doppa, J. R., Orr, W., Tadepalli, P., Fern, X. (2011). Inverting Grice's Maxims to Learn Rules from Natural Language Extractions. 2011 Conference on Neural Information Processing Systems (NIPS-2011). PDF Preprint.

Doppa, J. R., Sorower, M. S., Nasresfahani, M., Irvine, J., Orr, W., Dietterich, T. G., Fern, X., Tadepalli, P. (2011). Learning Rules from Incomplete Examples via Implicit Mention Models. 2011 Asian Conference on Machine Learning (ACML 2011). PDF Preprint.

Dereszynski, E., Hostetler, J., Fern, A., Dietterich, T., Hoang, T-T., Udarbe, M. (2011). Learning Probabilistic Behavior Models in Real-time Strategy Games. In Artificial Intelligence in Digital Entertainment (AIIDE-2011). PDF Preprint.

Dereszynski, E., Dietterich, T. G. (2011). Spatiotemporal Models for Anomaly Detection in Dynamic Environmental Monitoring Campaigns. ACM Transactions on Sensor Networks. 8(1): 3:1-3:26. PDF Preprint

Hutchinson, R., Liu, L-P., Dietterich, T. (2011). Incorporating Boosted Regression Trees into Ecological Latent Variable Models. In Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence (AAAI 2011). 1343-1348 PDF. This version corrects a typo in the published version.

Sorower, M. S., Dietterich, T. G., Doppa, J. R., Tadepalli, P., Fern, X. (2011). Learning Rules from Incomplete Examples via a Probabilistic Mention Model. IJCAI 2011 Workshop on Learning by Reading and its Applications in Intelligent Question-Answering. PDF Preprint.

Doppa, J. R., NasrEsfahani, M., Sorower, M. S., Irvine, J. Dietterich, T. G., Fern, X., Tadepalli, P. (2011). Learning Rules from Incomplete Examples via Observation Models. IJCAI 2011 Workshop on Learning by Reading and its Applications in Intelligent Question-Answering. PDF Preprint.

Lin, J., Larios, N., Lytle, D., Moldenke, A., Paasch, R., Shapiro, L., Todorovic, S., Dietterich, T. (2011). Fine-Grained Recognition for Arthropod Field Surveys: Three Image Collections. First Workshop on Fine-Grained Visual Categorization (CVPR-2011). PDF Preprint.

Larios, N., Lin, J., Zhang, M., Lytle, D., Moldenke, A., Shapiro, L. G., and Dietterich, T. G. (2011). Stacked Spatial-Pyramid Kernel: An Object-Class Recognition Method to Combine Scores from Random Trees, IEEE Workshop on Applications of Computer Vision. 329-335. PDF Preprint.

Bao, X., Dietterich, T. G. (2011). FolderPredictor: Reducing the Cost of Reaching the Right Folder. ACM Transactions on Intelligent Systems and Technology. 2(1), 8:1-8:23. DOI PDF Preprint.

Lytle, D. A., Martínez-Muñoz, G., Zhang, W., Larios, N., Shapiro, L., Paasch, R., Moldenke, A., Mortensen, E. A., Todorovic, S., Dietterich, T. G. (2010). Automated processing and identification of benthic invertebrate samples. Journal of the North American Benthological Society, 29(3), 867-874. PDF preprint.

Larios, N., Soran, B., Shapiro, L., Martínez-Muños, G., Lin, J., Dietterich, T. G. (2010). Haar Random Forest Features and SVM Spatial Matching Kernel for Stonefly Species Identification. IEEE International Conference on Pattern Recognition (ICPR-2010). In press. PDF Preprint.

Judah, K., Fern, A., Roy, S., Dietterich, T. (2010). Reinforcement Learning via Practice and Critique Advice. AAAI Conference on Artificial Intelligence (AAAI-10). 481-486. AAAI Press. PDF

Jensen, C., Lonsdale, H., Wynn, E., Cao, J., Slater, M., Dietterich, T. G. (2010). The life and times of files and information: a study of desktop provenance. pp. 767-776. In E. D. Mynatt, D. Schoner, G. Fitzpatrick, S. E. Hudson, K. Edwards, T. Rodden (Eds.) Proceedings of the 28th International Conference on Human Factors in Computing Systems, CHI 2010. PDF Preprint.

Barford, P., Dacier, M., Dietterich, T. G., Fredrikson, M., Giffin, J., Jajodia, S., Jha, S., Li, J., Liu, P., Ning, P., Ou, X., Song, D., Strater, L., Swarup, V., Tadda, G., Wang, C., Yen., J. (2010). Cyber SA: Situational Awareness for Cyber Defense. pp. 3-13. in Jajodia, S., Liu, P., Swarup, V., Wang, C. (Eds.) Cyber Situational Awareness, Springer. PDF Preprint.

Dietterich, T. G., Bao, X., Keiser, V., Shen, J. (2010). Machine Learning Methods for High Level Cyber Situation Awareness. pp. 227-247 in Jajodia, S., Liu, P., Swarup, V., Wang, C. (Eds.) Cyber Situational Awareness, Springer. PDF Preprint.

Shen, J., Dietterich, T. (2009). A Family of Large Margin Linear Classifiers and Its Application in Dynamic Environments. Statistical Analysis and Data Mining. 2(5-6): 328-345. PDF Preprint.

Keiser, V., Dietterich, T. G. (2009). Evaluating online text classification algorithms for email prediction in TaskTracer. Conference on Email and Anti-Spam. July 16-17, Mountain View, CA. PDF Preprint.

Zhang, X., Yoon, S., DiBona, P., Appling, D. S., Ding, L., Doppa, J. R., Green, D., Guo, J. K., Kuter, U. Levine, G., MacTavish, R. L., McFarlane, D., Michaelis, J. R., Mostafa, H., Ontanon, S., Parker, C., Radhakrishnan, J., Rebguns, A., Shrestha, B., Song, Z., Trewhitt, E. B., Zafar, H., Zhang, C., Corkill, D., DeJong, G., Dietterich, T. G., Kambhampati, S., Lesser, V., McGuinness, D. L., Ram, A., Spears, D., Tadepalli, P. Whitaker, E. T., Wong, W-K., Hendler, J. A., Hofmann, M. O., Whitebread, K. (2009). An Ensemble Learning and Problem Solving Architecture for Airspace Management. Proceedings of the 2009 Conference on Innovative Applications of Artificial Intelligence (IAAI-2009). PDF Preprint.

Dietterich, T. G. (2009). Machine Learning in Ecosystem Informatics and Sustainability. Abstract of Invited Talk. Proceedings of the 2009 International Joint Conference on Artificial Intelligence (IJCAI-2009). Pasadena, CA. PDF Preprint.

Judah, K., Dietterich, T., Fern, A., Irvine, J., Slater, M., Tadepalli, P., Gervasio, M., Ellwood, C., Jarrold, B., Brdiczka, O., Blythe, J. (2009). User Initiated Learning for Adaptive Interfaces. In IJCAI2009 Workshop on Intelligence and Interaction. PDF Preprint.

Martínez-Muñoz, G., Zhang, W., Payet, N., Todorovic, S., Larios, N., Yamamuro, A., Lytle, D., Moldenke, A., Mortensen, E., Paasch, R., Shapiro, L., Dietterich, T. (2009). Dictionary-Free Categorization of Very Similar Objects via Stacked Evidence Trees.. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR-2009), Miami Beach, FL. PDF Preprint.

Zhang, W., Surve, A., Fern, X., Dietterich, T. (2009). Learning Non-Redundant Codebooks for Classifying Complex Objects. In International Conference on Machine Learning (ICML-2009). Montreal, Canada. 1241-1248. PDF Preprint.

Stumpf, S., Rajaram, V., Li, L., Wong, W-K., Burnett, M., Dietterich, T., Sullivan, E., Herlocker, J. (2009). Interacting Meaningfully with Machine Learning Systems: Three Experiments. International Journal on Human-Computer Studies. DOI 10.1016/j.ijhcs.2009.03.004 PDF Preprint.

Shen, J., Dietterich, T. (2009). A Family of Large Margin Linear Classifiers and Its Applications in Dynamic Environments. Proceedings of the SIAM International Conference on Data Mining 2009 (SDM-09), pages 164-172. PDF Preprint.

Shen, J., Irvine, J., Bao, X., Goodman, M., Kolibab, S., Tran, A., Carl, F., Kirschner, B., Stumpf, S., Dietterich, T. (2009). Detecting and Correcting User Activity Switches: Algorithms and Interfaces. In Proceedings of the International Conference on Intelligent User Interfaces (IUI-2009). PDF Preprint.

Shen, J., Fitzhenry, E., Dietterich, T. (2009). Discovering Frequent Work Procedures From Resource Connections. In Proceedings of the International Conference on Intelligent User Interfaces (IUI-2009). PDF Preprint.

Natarajan, S., Tadepalli, P., Dietterich, T. and Fern, A. (2008). Learning First-Order Probabilistic Models with Combining Rules, Annals of Mathematics and Artificial Intelligence, Special issue on Probabilistic Relational Learning, 54 (1-3): 223-256. PDF Preprint.

Sarpola, M.J., Paasch, R.K., Dietterich, T.G., Lytle, D.A., Mortensen, E. N., Moldenke, A.R., and Shapiro, L. (2008). An aquatic insect imaging device to automate insect classification, Transactions of the American Society of Agricultural and Biological Engineers, 51 (6): 2217-2225. PDF Preprint.

Zhang, W., Dietterich, T. G. (2008). Learning Visual Dictionaries and Decision Lists for Object Recognition. 19th International Conference on Pattern Recognition (ICPR-08). 1-4. DOI 10.1109/ICPR.2008.4761769. PDF Preprint.

Dietterich, T. G., Hao, G., Ashenfelter, A. (2008). Gradient Tree Boosting for Training Conditional Random Fields. Journal of Machine Learning Research. 9, 2113-2139. PDF Preprint.

Dietterich, T. G., Domingos, P., Getoor, L., Muggleton, S. Tadepalli, P. (2008). Structured machine learning: the next ten years. Machine Learning. 73(1) 3-23. DOI: 10.1007/s10994-008-5079-1 PDF Preprint.

Wynkoop, M., Dietterich, T. (2008). Learning MDP Action Models Via Discrete Mixture Trees. In Machine Learning and Knowledge Discovery in Databases. Lecture Notes in Computer Science Volume 5212/2008, 597-612. Berlin: Springer. PDF Preprint.

Mehta, N., Ray, S., Tadepalli, P., Dietterich, T. (2008). Automatic Discovery and Transfer of MAXQ Hierarchies. International Conference on Machine Learning (ICML-2008) PDF Preprint.

Dietterich, T. G., Bao, X. (2008). Integrating Multiple Learning Components Through Markov Logic. Twenty-Third Conference on Artificial Intelligence (AAAI-2008). 622-627. PDF Preprint.

Larios, N., Deng, H., Zhang, W., Sarpola, M., Yuen, J., Paasch, R., Moldenke, A., Lytle, D., Ruiz Correa, S., Mortensen, E., Shapiro, L., Dietterich, T. (2008). Automated Insect Identication through Concatenated Histograms of Local Appearance Features. Machine Vision and Applications, 19 (2):105-123. PDF Preprint.

Stumpf, S., Fitzhenry, E., Dietterich, T. (2007). The Use of Provenance in Information Retrieval.. Workshop on Principles of Provenance (PROPR), Edinburgh, Scotland, 19-20 November, 2007. PDF Preprint.

Dietterich, T. G. (2007). Machine Learning in Ecosystem Informatics. Proceedings of the Tenth International Conference on Discovery Science. Lecture Notes in Artificial Intelligence Volume 4755, Springer, Berlin. PDF Preprint.

Peterson, C., Paasch, R. K., Ge, P., Dietterich, T. G. (2007). Product innovation for interdisciplinary design under changing requirements. International Conference on Engineering Design (ICED2007), Paris, France. PDF preprint.

Dereszynski, E., Dietterich, T. (2007). Probabilistic Models for Anomaly Detection in Remote Sensor Data Streams. Proceedings of the 23rd Conference on Uncertainty in Artificial Intelligence (UAI-2007). 75-82. PDF preprint.

E. N. Mortensen, E. L. Delgado, H. Deng, D. Lytle, A. Moldenke, R. Paasch, L. Shapiro, P. Wu, W. Zhang, T. G. Dietterich (2007). Pattern Recognition for Ecological Science and Environmental Monitoring: An Initial Report. In N. MacLeod and M. O'Neill (Eds.) Automated Taxon Identification in Systematics. 189-206. CRC Press, Boca Raton. PDF preprint.

Deng, H., Zhang, W., Mortensen, E., Dietterich, T. (2007). Principal Curvature-based Region Detector for Object Recognition. IEEE Conference on Computer Vision and Pattern Recognition (CVPR-2007). Minneapolis, MN. 1-4. DOI 10.1109/CVPR.2007.382972. PDF Preprint.

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. (2007). Automated Insect Identification through Concatenated Histograms of Local Appearance Features. IEEE Workshop on Applications of Computer Vision (WACV-2007), 26-32. Austin, TX. PDF Preprint.

Stumpf, S., Rajaram, V., Li, L., Burnett, M., Dietterich, T., Sullivan, E., Drummond, R., Herlocker, J. (2007). Toward Harnessing User Feedback for Machine Learning. In International Conference on Intelligent User Interfaces (IUI-2007), Honolulu, HI. pp. 82-91. ACM Press. PDF Preprint.

Shen, J., Dietterich, T. (2007). Active EM to Reduce Noise in Activity Recognition. In International Conference on Intelligent User Interfaces (IUI-2007), Honolulu, HI. pp. 132-140. ACM Press. PDF Preprint.

Shen, J., Li, L., Dietterich, T. G. (2007). Real-Time Detection of Task Switches of Desktop Users. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI-07). Hyderabad, India. pp. 2868-2873. PDF preprint.

Deng, H., Mortensen, E. N., Shapiro, L., Dietterich, T. G. (2006). Reinforcement Matching Using Region Context. In S. Lucey and T. Chen (Eds.) Beyond Patches. Workshop at IEEE Conference on Computer Vision and Pattern Recognition. IEEE. New York. PDF preprint.

Zhang, W., Deng, H., Dietterich, T. G., Mortensen, E. N. (2006). A Hierarchical Object Recognition System Based on Multi-scale Principal Curvature Regions. Proceedings of the International Conference on Pattern Recognition, Vol. I. 778-782. PDF Preprint.

Langford, W. T., Gergel, S. E., Dietterich, T. G., Cohen, W. (2006). Map misclassification can cause large errors in landscape pattern indices: Examples from habitat fragmentation. Ecosystems, 9 (3), 474-488. PDF Preprint.

Bao, X., Herlocker, J., Dietterich, T. (2006). Fewer clicks and less frustration: Reducing the cost of reaching the right folder. In 2006 International Conference on Intelligent User Interfaces. 178-185. Sydney, Australia. PDF preprint.

Shen, J., Li, L., Dietterich, T., Herlocker, J. (2006). A Hybrid Learning System for Recognizing User Tasks from Desktop Activities and Email Messages. In 2006 International Conference on Intelligent User Interfaces. 86-92. Sydney, Australia. PDF postprint. (Corrected bibliography after publication.)

Marx, Z., Rosenstein, M. T., Kaelbling, L. P., Dietterich, T. G. (2005). Transfer learning with an ensemble of background tasks. NIPS 2005 Workshop on Transfer Learning, Whistler, BC. PDF preprint.

Rosenstein, M. T., Marx, Z., Kaelbling, L. P., Dietterich, T. G. (2005). To transfer or not to transfer. NIPS 2005 Workshop on Transfer Learning, Whistler, BC. PDF preprint.

Altendorf, E., Restificar, E., Dietterich, T. G. (2005). Learning from sparse data by exploiting monotonicity constraints. Proceedings of the 21st Conference on Uncertainty in Artificial Intelligence, Edinburgh, Scotland. PDF preprint.

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, Bonn, Germany. PDF preprint.

Bayer-Zubek, V., Dietterich, T. G. (2005). Integrating Learning from Examples into the Search for Diagnostic Policies. Journal of Artificial Intelligence Research, 24, 263-303. JAIR web page.

Stumpf, S., Bao, X., Dragunov, A., Dietterich, T., Herlocker, J., Johnsrude, K., Li, L., Shen, J. (2005). Predicting User Tasks: I Know What You're Doing!. In Workshop on Human Comprehensible Machine Learning. Twentieth National Conference on Artificial Intelligence (AAAI-05). Pittsburgh, PA, July 9-13, 2005. PDF preprint.

Dragunov, A. N., Dietterich, T. G., Johnsrude, K., McLaughlin, M., Li, L., Herlocker, J. L. (2005). TaskTracer: A Desktop Environment to Support Multi-tasking Knowledge Workers. International Conference on Intelligent User Interfaces (IUI-2005), (pp. 75-82). ACM Press. PDF preprint.

Dietterich, T. G. Learning and Reasoning. Technical report, School of Electrical Engineering and Computer Science, Oregon State University. PDF version gzipped postscript version.

Barto, A., Dietterich, T. G. (2004). Reinforcement learning and its relationship to supervised learning. In J. Si, A. G. Barto, W. B. Powell, D. Wunsch II (Eds.) Handbook of Learning and Approximate Dynamic Programming. pp. 47-64. Wiley Interscience/IEEE Press, Piscataway, NJ. PDF preprint.

Dietterich, T. G., Ashenfelter, A., Bulatov, Y. (2004). Training Conditional Random Fields via Gradient Tree Boosting. International Conference on Machine Learning, 217-224, Banff, Canada PDF preprint.

Wu, P., Dietterich, T. G. (2004). Improving SVM Accuracy by Training on Auxiliary Data Sources. International Conference on Machine Learning, 871-878, Banff, Canada PDF preprint.

Valentini, G., Dietterich, T. G. (2004). Bias-variance analysis of Support Vector Machines for the development of SVM-based ensemble methods. Journal of Machine Learning Research, 5, 725-775. PDF preprint. gzipped postscript preprint.

Valentini, G. and Dietterich, T. G. (2003). Low Bias Bagged Support Vector Machines. International Conference on Machine Learning, ICML-2003, Washington, DC, 752-759. PDF preprint. gzipped postscript.

Wang, X. and Dietterich, T. G. (2003). Model-based Policy Gradient Reinforcement Learning. International Conference on Machine Learning, ICML-2003, Washington, DC, 776-783. Postscript preprint.

Dietterich, T. G. (2003). Machine Learning. In Nature Encyclopedia of Cognitive Science, London: Macmillan, 2003. Postscript Preprint PDF Version

Dietterich, T. G. and Wang, X. (2002). Batch value function approximation via support vectors. In Dietterich, T. G., Becker, S., Ghahramani, Z. (Eds.) Advances in Neural Information Processing Systems 14. (pp. 1491-1498). Cambridge, MA: MIT Press. Postscript preprint.

Wang, X. and Dietterich, T. G. (2002). Stabilizing value function approximation with the BFBP algorithm. In T. G., Becker, S., Ghahramani, Z. (Eds.) Advances in Neural Information Processing Systems 14. (pp. 1587-1594). Cambridge, MA: MIT Press. Postscript preprint.

Margineantu, D. D. and Dietterich, T. G. (2002) Improved class probability estimates from decision tree models. in D. D. Denison, M. H. Hansen, C. C. Holmes, B. Mallick, and B. Yu (Eds.) Nonlinear Estimation and Classification; Lecture Notes in Statistics, 171, pp. 169-184. New York: Springer-Verlag. Postscript preprint. PDF preprint. © Springer-Verlag.

Dietterich, T. G. (2002). Machine Learning for Sequential Data: A Review. In T. Caelli (Ed.) Structural, Syntactic, and Statistical Pattern Recognition; Lecture Notes in Computer Science, Vol. 2396. (pp. 15-30). Springer-Verlag. Postscript preprint. PDF preprint. © Springer-Verlag.

Fountain, T., Dietterich, T., and Sudyka, B. (2002). Data mining for manufacturing control: An application in optimizing IC test. Chapter 13 of B. Nebel and G. Lakemeyer (Eds.) Exploring Artificial Intelligence in the New Millenium. Morgan-Kaufmann. Postscript preprint.

Busquets, D., Lopez de Mantaras, R., Sierra, C., and Dietterich, T. G. (2002). Reinforcement learning for landmark-based robot navigation. Proceedings of Autonomous Agents and Multi-Agent Systems. (pp. 841-842). ACM Press. Postscript (Longer version is available as a technical report; see below)

Dietterich, T. G., Becker, S., and Ghahramani, Z. (eds.) (2002). Advances in Neural Information Processing Systems 14. MIT Press, Cambridge, MA. Online Proceedings.

Dietterich, T. G., Busquets, D., Lopez de Mantaras, R., Sierra, C. (2002). Action Refinement in Reinforcement Learning by Probability Smoothing. In Proceedings of the International Conference on Machine Learning. (pages 107-114) Postscript Preprint. PDF Preprint.

Zubek, V. B., Dietterich, T. G. (2002). Pruning Improves Heuristic Search for Cost-Sensitive Learning. In Proceedings of the International Conference on Machine Learning. (pages 27-34) Postscript Preprint. PDF Preprint.

Dietterich, T. G. (2002). Ensemble Learning. In The Handbook of Brain Theory and Neural Networks, Second edition, (M.A. Arbib, Ed.), Cambridge, MA: The MIT Press, 2002. 405-408. Postscript Preprint.

Valentini, G., Dietterich, T. G. (2002). Bias-Variance Analysis and Ensembles of SVM. In J. Kittler and F. Roli (Ed.) Third International Workshop on Multiple Classifier Systems, Lecture Notes in Computer Science, 2364. (pp. 222-231) New York: Springer Verlag. Postscript preprint © Springer-Verlag.

Busquets, D., Lopez de Mantaras, R., Sierra, C., and Dietterich, T. G. (2001). Reinforcement learning for landmark-based robot navigation. Technical Report, Department of Computer Science, Oregon State University. Postscript

Bakiri, G., Dietterich, T. G. (2001). Achieving high-accuracy text-to-speech with machine learning. In B. Damper (Ed.) Data mining in speech synthesis. Kluwer Academic Publishers, Boston, MA. Postscript preprint.

Zubek, V. B., Dietterich, T. G. (2001). Two Heuristics for Solving POMDPs Having a Delayed Need to Observe. To appear in Proceedings of the IJCAI Workshop on Planning under Uncertainty and Incomplete Information. August 6, 2001. Seattle, WA. Postscript preprint.

Margineantu, D. and Dietterich, T. G. (2001). Lazy Class Probability Estimators. In 33rd Symposium on the Interface of Computing Science and Statistics, Costa Mesa, California.

Leen, T. K., Dietterich, T. G., and Tresp, V. (2001) Advances in Neural Information Processing Systems, 13, Cambridge, MA: MIT Press.

Dietterich, T. G. (2000). The Divide-and-Conquer Manifesto In Algorithmic Learning Theory 11th International Conference (ALT 2000) (pp. 13-26). New York: Springer-Verlag. Postscript Preprint. © Springer-Verlag.

Dietterich, T. G. (2000). Hierarchical reinforcement learning with the MAXQ value function decomposition. Journal of Artificial Intelligence Research, 13, 227-303. Compressed postscript. Also available from my HTTP directory as Gzipped postscript

Wang, X., Dietterich, T. G. (2000). 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, 1-58. Gzipped postscript.

Dietterich, T. G. (2000). Machine Learning. In David Hemmendinger, Anthony Ralston and Edwin Reilly (Eds.), The Encyclopedia of Computer Science, Fourth Edition, Thomson Computer Press. 1056-1059.

Zubek, V. B. and Dietterich, T. G. (2000) A POMDP Approximation Algorithm that Anticipates the Need to Observe. In Proceedings of the Pacific Rim Conference on Artificial Intelligence (PRICAI-2000); Lecture Notes in Computer Science (pp. 521-532). New York: Springer-Verlag. Postscript Preprint. © Springer-Verlag.

Fountain, T., Dietterich, T. G., Sudyka, B. (2000). Mining IC Test Data to Optimize VLSI Testing. In Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. (pp. 18-25). ACM Press. PDF preprint. Winner of Award for Best Application Paper (Research Track).

Dietterich, T. G. (2000). An Overview of MAXQ Hierarchical Reinforcement Learning. In B. Y. Choueiry and T. Walsh (Eds.) Proceedings of the Symposium on Abstraction, Reformulation and Approximation SARA 2000, Lecture Notes in Artificial Intelligence (pp. 26-44), New York: Springer Verlag. Postscript preprint © Springer-Verlag.

Chown, E., Dietterich, T. G. (2000). A Divide-and-Conquer Approach to Learning From Prior Knowledge. In International Conference on Machine Learning, ICML-2000 (pp. 143-150). Postscript preprint.

Margineantu, D. D., Dietterich, T. G. (2000). Bootstrap Methods for the Cost-Sensitive Evaluation of Classifiers . In International Conference on Machine Learning, ICML-2000 (pp. 582-590). Postscript preprint

Dietterich, T. G. (2000). Ensemble Methods in Machine Learning. In J. Kittler and F. Roli (Ed.) First International Workshop on Multiple Classifier Systems, Lecture Notes in Computer Science (pp. 1-15). New York: Springer Verlag. Postscript preprint. PDF preprint. © Springer-Verlag.

Dietterich, T. G., (2000). An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine Learning, 40 (2) 139-158. Postscript preprint PDF preprint.

Dietterich, T. G. (2000). State abstraction in MAXQ hierarchical reinforcement learning. In Advances in Neural Information Processing Systems, 12. S. A. Solla, T. K. Leen, and K.-R. Muller (eds.), 994-1000, MIT Press. Postscript Preprint.

Dietterich, T. G. (1999). Machine Learning. In Rob Wilson and Frank Keil (Eds.) The MIT Encyclopedia of the Cognitive Sciences, MIT Press. 497-498.

Margineantu, D., Dietterich, T. G. (1999). Learning Decision Trees for Loss Minimization in Multi-Class Problems. Technical report 99-30-03. Department of Computer Science, Oregon State University Postscript file.

Wang, X., Dietterich, T. G. (1999). Efficient Value Function Approximation Using Regression Trees. In Proceedings of the IJCAI Workshop on Statistical Machine Learning for Large-Scale Optimization, Stockholm, Sweden. Postscript file.

Dietterich, T. G. (1998). The MAXQ method for hierarchical reinforcement learning. 1998 International Conference on Machine Learning. (118-126). Morgan Kaufmann, San Francisco. Postscript preprint. Note: This version has some errors corrected compared to the version that appears in the proceedings. In particular, Figure 1 is fixed.

Dietterich, T. G., (1998). Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms. Neural Computation, 10 (7) 1895-1924. Postscript preprint. (Revised December 30, 1997).

Chown, E., Dietterich, T. G. (1997). A comparison of neural network and process-based models for vegetation distribution under global climate change. Technical Report. Postscript preprint.

Zhang, W., Dietterich, T. G. (1997). Solving Combinatorial Optimization Tasks by Reinforcement Learning: A General Methodology Applied to Resource-Constrained Scheduling. Technical Report. Department of Computer Science, Oregon State University. Postscript preprint.

Dietterich, T. G., (1997). Machine Learning Research: Four Current Directions AI Magazine. 18 (4), 97-136. Postscript preprint. PDF preprint.

Dietterich, T. G. (1997). Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition. Technical report. Gzipped postscript file.

Dietterich, T. G. (1997). Fundamental Experimental Research in Machine Learning. A section of the document Basic Topics in Experimental Computer Science edited by John McCarthy. HTML version or gzipped postscript version. Kong, E. G., and Dietterich, T. G. (1997). Probability estimation using error-correcting output coding. IASTED International Conference: Artificial Intelligence and Soft Computing, Banff, Canada. Postscript preprint.

Margineantu, D., Dietterich, T. G. (1997). Pruning Adaptive Boosting. Fourteenth International Conference on Machine Learning. Morgan Kaufmann, San Francisco. 211-218. Postscript preprint PDF preprint.

Tadepalli, P., Dietterich, T. G. (1997). Hierarchical Explanation-Based Reinforcement Learning. Fourteenth International Conference on Machine Learning. Morgan Kaufmann, San Francisco. 358-366. Postscript preprint.

Dietterich, T. G., Flann, N. S. (1997). Explanation-based Learning and Reinforcement Learning: A Unified View. Machine Learning, 28, 169-210. Postscript preprint. . PDF preprint. See below for short version that appeared in the 1995 Machine Learning Conference.

Dietterich, T. G., Lathrop, R. H., Lozano-Perez, T. (1997) Solving the multiple-instance problem with axis-parallel rectangles. Artificial Intelligence, 89(1-2), 31-71. PDF file (The last 3 figures are not available online; Note: this version contains a corrected Table 4, in which the confidence interval for C4 is fixed.)

Dietterich, T. G., (1996). Editorial Machine Learning 24 (2), 1-3. Postscript preprint.

Dietterich, T. G., Kearns, M., Mansour, Y., (1996). Applying the weak learning framework to understand and improve C4.5. Proceedings of the Thirteenth International Conference on Machine Learning, 96-104. PDF preprint.

Zhang, W., Dietterich, T. G., (1996). High-Performance Job-Shop Scheduling With A Time-Delay TD(lambda) Network. D. S. Touretzky, M. C. Mozer, and M. E. Hasselmo (Eds.) Advances in Neural Information Processing Systems, 8, 1024-1030. Postscript preprint.

Almuallim, H., Dietterich, T. G. (1995). A study of maximal-coverage learning algorithms. In D. Wolpert (Ed.) The Mathematics of Generalization: Proceedings of the SFI/CNLS Workshop on Formal Approaches to Supervised Learning . Reading, MA: Addison-Wesley. 279-314. Institute. Postscript preprint.

Dietterich, T. G. (1995) Overfitting and under-computing in machine learning. Computing Surveys, 27(3), 326-327. Postscript preprint

Wettschereck, D., Dietterich, T. G. (1995) An experimental comparison of the nearest-neighbor and nearest-hyperrectangle algorithms. Machine Learning, 19(1), 5-28. Postscript preprint.

Dietterich, T. G., Hild, H., and Bakiri, G., (1995) A comparison of ID3 and backpropagation for English text-to-speech mapping. Machine Learning, 18(1), 51-80. Postscript preprint.

Dietterich, T. G., Bakiri, G. (1995) Solving Multiclass Learning Problems via Error-Correcting Output Codes. Journal of Artificial Intelligence Research 2: 263-286. Postscript file.

Zhang, W., Dietterich, T. G., (1995). A Reinforcement Learning Approach to Job-shop Scheduling. Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence. (1114-1120). San Francisco, CA: Morgan-Kaufmann. Postscript preprint.

Dietterich, T. G., Flann, N. S., (1995). Explanation-based Learning and Reinforcement Learning: A Unified View. In Proceedings of the 12th International Conference on Machine Learning (pp. 176-184). Tahoe City, CA. San Francisco: Morgan Kaufmann. Poscript preprint. See above for journal version.

Kong, E. B., Dietterich, T. G., (1995). Error-Correcting Output Coding Corrects Bias and Variance. In Proceedings of the 12th International Conference on Machine Learning (pp. 313-321). Tahoe City, CA. San Francisco: Morgan Kaufmann. Poscript preprint.

Zhang, W., Dietterich, T. G., (1995). Value Function Approximations and Job-Shop Scheduling. In J. A. Boyan, A. W. Moore, and R. S. Sutton (Eds.) Proceedings of the Workshop on Value Function Approximation. Carnegie-Mellon University, School of Computer Science, Report Number CMU-CS-95-206. Postscript file.

Dietterich, T. G., Kong, E. B., (1995). Machine Learning Bias, Statistical Bias, and Statistical Variance of Decision Tree Algorithms. Technical Report. Department of Computer Science, Oregon State University. Poscript file.

Jain, A. N., Dietterich, T. G., Lathrop, R. H., Chapman, D., Critchlow, R. E., Bauer, B. E., Webster, T. A., Lozano-Perez, T. (1994). Compass: A shape-based machine learning tool for drug design. Computer-Aided Molecular Design, 8 (6) 635-652.

Almuallim, H., Dietterich, T. G. (1994) Learning boolean concepts in the presence of many irrelevant features. Artificial Intelligence, 69(1-2): 279-306. Postscript preprint.

Dietterich, T. G., Jain, A., Lathrop, R., Lozano-Perez, T. (1994). A comparison of dynamic reposing and tangent distance for drug activity prediction. Advances in Neural Information Processing Systems, 6. San Mateo, CA: Morgan Kaufmann. 216-223. Postscript preprint.

Wettschereck, D., Dietterich, T. G. (1994). Locally adaptive nearest neighbor algorithms. Advances in Neural Information Processing Systems, 6. San Mateo, CA: Morgan Kaufmann. 184-191. Postscript preprint.

Kong, E. B., Dietterich, T. G. (1994). Why error-correcting output coding works. Also cited as "Why error-correcting output coding works with decision trees." Unpublished manuscript. PDF. In retrospect, I would reinterpret Figure 10 as showing that the advantage of ECOC over standard methods disappears as the learning algorithm becomes more local and increases as the learning algorithm becomes more global. So I view this as showing that the decision-boundary alignment hypothesis is likely to be at least partially correct.

Bakiri, G., Dietterich, T. G. (1993). Performance comparison between human engineered and machine learned letter-to-sound rules for English: A machine learning success story. 18th International Conference on the Applications of Computers and Statistics to Science and Society. Cairo, Egypt. Postscript preprint.

Almuallim, H., Dietterich, T. G. (1992). Efficient algorithms for identifying relevant features. Proceedings of the Ninth Canadian Conference on Artificial Intelligence (pp. 38-45). Vancouver, BC: Morgan Kaufmann. May 11-15. Postscript preprint.

Almuallim, H., Dietterich, T. G. (1992). On learning more concepts. In Proceedings of the Ninth International Conference on Machine Learning, (pp. 11-19), Aberdeen, Scotland: Morgan-Kaufmann. Postscript preprint.

Wettschereck, D., Dietterich, T. G. (1992) Improving the performance of radial basis function networks by learning center locations. In Moody, J. E., Hanson, S. J., and Lippmann, R. P. (Eds.) Advances in Neural Information Processing Systems, 4. (pp. 1133-1140) San Mateo, CA: Morgan Kaufmann. Postscript preprint.

Dietterich, T. G. (1991) Knowledge compilation: Bridging the gap between specification and implementation. IEEE Expert, 6 (2) 80-82. Postscript preprint.

Dietterich, T. G., Bakiri, G. (1991) Error-correcting output codes: A general method for improving multiclass inductive learning programs. Proceedings of the Ninth National Conference on Artificial Intelligence (AAAI-91) (pp. 572-577). Anaheim, CA: AAAI Press. Postscript preprint.

Almuallim, H., Dietterich, T. G. (1991) Learning with many irrelevant features. Proceedings of the Ninth National Conference on Artificial Intelligence (AAAI-91) (pp. 547-552). Anaheim, CA: AAAI Press. Postscript preprint.

Dietterich, T. G. (1991). Do Hidden Units Implement Error-Correcting Codes? Technical Report. Postscript file

Cerbone, G., Dietterich, T. G. (1991). Symbolic Methods in Numerical Optimization. Technical Report 91-30-7, Department of Computer Science, Oregon State University, Corvallis, OR. PDF Scan.

Shavlik, J. and Dietterich, T. G. (1990). Readings in Machine Learning. Morgan Kaufmann Publishers, San Francisco, CA. Introductory Article and Contents. Order from Amazon.

Dietterich, T. G. (1990). Machine Learning. Annual Review of Computer Science, 4: 255-306. Postscript preprint with no figures.

Dietterich, T. G., (1990). Editorial: Exploratory Research in Machine Learning. Machine Learning 5 (1), 5-10. Postscript preprint.

Dietterich, T. G., Hild, H., Bakiri, G. (1990) A comparative study of ID3 and backpropagation for English text-to-speech mapping. Proceedings of the 1990 Machine Learning Conference, Austin, TX. 24-31. Postscript preprint.

Cerbone, G., Dietterich, T. G., (1990) Inductive and numerical methods in knowledge compilation. Proceedings of CRIB-90. Menlo Park, CA: Price Waterhouse.

Flann, N. S., and Dietterich, T. G., (1989) A study of explanation-based methods for inductive learning. Machine Learning, 4 (2), 187-226.

Dietterich, T. G., (1989) Limitations of Inductive Learning. Proceedings of the Sixth International Workshop on Machine Learning, Ithaca, NY. San Mateo, CA: Morgan Kaufmann. 124-128. Postscript preprint.

Koff, Caroline N. (1988). A specialized ATMS for Equivalence Relations. M.S. Thesis. Technical report 88-80-7. Department of Computer Science, Oregon State University. PDF Scan.

Ullman, D. G., Dietterich, T. G., and Stauffer, L. A. (1988). A model of the mechanical design process based on empirical data. Artificial Intelligence in Engineering, Design, and Manufacturing, 2 (1), 33-52.

Dietterich, T. G., and Flann, N. S., (1988). An inductive approach to solving the imperfect theory problem. Proceedings of the AAAI Spring Symposium Series: Explanation-based Learning, 42-46.

Flann, N. S., Dietterich, T. G., (1988). Induction over Explanations: A method that exploits domain knowledge to learn from examples. Technical Report 88-30-3. Department of Computer Science, Oregon State University. PDF Scan.

Koff, C. N., Flann, N. S., and Dietterich, T. G., (1988). A specialized ATMS for equivalence relations. Proceedings of the National Conference on Artificial Intelligence (AAAI-88), St. Paul, MN. Los Altos, CA: Morgan-Kaufmann. 182-187.

Koff, C. N., Flann, N. S., and Dietterich, T G., (1988). An efficient ATMS for equivalence relations.. Technical Report 88-30-1. Department of Computer Science, Oregon State University. PDF Scan.

Dietterich, T. G., Bennett, J. S. (1988). Varieties of Operationality. Technical Report 88-30-6, Department of Computer Science, Oregon State University. PDF Tech Report.

Ullman, D. G., and Dietterich, T. G. (1987). Toward Expert CAD, ASME, Computers in Mechanical Engineering, 6(3), 56-70.

Ullman, D. G., and Dietterich, T. G. (1987). Mechanical design methodology: Implications on future developments of computer-aided design and knowledge-based systems. Engineering With Computers, (2), 21-29.

Flann, N. S., Dietterich, T. G., and Corpron, D. R., (1987). Forward chaining logic programming with the ATMS. In Proceedings of the National Conference on Artificial Intelligence (AAAI-87), Seattle, WA. Los Altos, CA: Morgan-Kaufmann, 24-29. Also Technical Report 87-30-2, Department of Computer Science, Oregon State University. PDF Scan.

Dietterich, T. G., and Ullman, D. G., (1987) FORLOG: A Logic-Based Architecture for Design, in Expert Systems in Computer-Aided Design, North-Holland, 1987, 1-24, and presented at IFIP WG5.2 Working Conference on Expert Systems in Computer-Aided Design, Sydney, Australia, February, 1987. PDF Tech Report.

Dietterich, T. G., and D'Ambrosio, B. (1987). Artificial Intelligence at OSU. Technical Report 87-30-5, Department of Computer Science, Oregon State University. PDF Tech Report.

Corpron, Daniel R. (1987). Disjunctions in Forward Chaining Logic Programming. M.S. Thesis. Department of Computer Science. Oregon State University. Technical report 87-30-1. PDF Scan.

Dietterich, T. G., and Michalski, R. S., (1986). Learning to Predict Sequences , in Machine Learning: An Artificial Intelligence Approach, Volume II, Michalski, R. S., Carbonell, J., and Mitchell, T. M., (eds.), Palo Alto: Tioga, 63-106.

Dietterich, T. G., (1986). Learning at the knowledge level, Machine Learning, 1(3) 287-316. Postscript preprint.

Dietterich, T. G. (1986). A Knowledge-Level Analysis of Learning Systems. Technical Report 87-30-4, Department of Computer Science, Oregon State University, Corvallis, OR. PDF tech report.

Dietterich, T. G., (1986). Induction: Weak but essential (commentary on Schank, Collins, and Hunter), Behavioral and Brain Sciences, 9 (4), 1986, 654-655. Postscript preprint.

Flann, N. and Dietterich, T. G., (1986). Selecting appropriate representations for learning from examples. In Proceedings of the National Conference on Artificial Intelligence: AAAI-86, Philadelphia, PA. Los Altos, CA: Morgan-Kaufmann, 460-466. PDF of technical report.

Dietterich, T. G., and Bennett, J. S., (1986). The Test Incorporation Hypothesis and the Weak Methods, Technical Report TR 86-30-4, Department of Computer Science, Oregon State University, Corvallis, OR. PDF version.

Dietterich, T. G., and Bennett, J. S. (1986). The Test Incorporation Theory of Problem Solving, In Proceedings of the Workshop on Knowledge Compilation, Department of Computer Science, Oregon State University, Corvallis, OR. Technical Report. PDF version.

Dietterich, T. G., (Ed.), (1986). Proceedings of the Workshop on Knowledge Compilation, Technical report, Department of Computer Science, Oregon State University, Corvallis, OR.

Dietterich, T. G., Flann, N. S., and Wilkins, D. C., (1986). A Summary of Machine Learning Papers from IJCAI-85, Machine Learning, 1 (2), 227-242. PDF Preprint.

Flann, N. S., Dietterich, T. G. (1986). Two Short Papers on Machine Learning. Tech. Report 86-30-3. Department of Computer Science, Oregon State University. PDF Scan.

Ullman, D. G., Stauffer, L. A., Dietterich, T. G. (1986). Prelimionary results of an experimental study of the mechanical design process. Technical Report 86-30-9. Department of Computer Science, Oregon State University. PDF Scan

Dietterich, T. G., and Michalski, R. S., (1985). Discovering patterns in sequences of events, Artificial Intelligence, 25, 187-232.

Dietterich, T. G., (1984). Constraint-Propagation Techniques for Theory-Driven Data Interpretation, Doctoral Dissertation, Rep. No. STAN-CS-84-1030, Department of Computer Science, Stanford University, Stanford, California.

Dietterich, T. G., (1984). Learning about systems that contain state variables, Proceedings of AAAI-84, Austin, Texas, 96-100.

Dietterich, T. G., and Buchanan, B. G., (1983). The role of the critic in learning systems, in Rissland, E. W., Arbib, M., and Selfridge, O., Adaptive Control of Ill-defined Systems, Plenum, 127-148.

Dietterich, T. G., and Michalski, R. S., (1983). A comparative review of selected methods for learning from examples, Chapter 3 of Machine Learning: An Artificial Intelligence Approach, Michalski, R. S., Carbonell, J., and Mitchell, T. M., (eds.), Palo Alto: Tioga, 41-82.

Dietterich, T. G., London, R. L., Clarkson, K., and Dromey, G. (1982). Learning and inductive inference. Chapter XIV in Cohen, P. R., and Feigenbaum, E. A., The Handbook of Artificial Intelligence, Vol. III, 323-512, Los Altos, CA: William Kaufmann.

Dietterich, T. G., and Michalski, R. S. (1981). Inductive learning of structural descriptions: Evaluation criteria and comparative review of selected methods. Artificial Intelligence, 16, 257-294.

Dietterich, T. G., (1980). Applying general induction methods to the card game Eleusis, Proceedings of the National Conference on Artificial Intelligence, AAAI-80, Stanford, California, 218-220. Scanned PDF.

Dietterich, T. G., (1980). The Methodology of Knowledge Layers for Inducing Descriptions of Sequentially Ordered Events, Master's Thesis, Rep. No. UIUCDCS-1024, Department of Computer Science, University of Illinois, Urbana, Illinois.

Dietterich, T. G., and Michalski, R. S., (1979). Learning and generalization of characteristic desciptions. Proceedings of the Sixth International Joint Conference on Artificial Intelligence, Tokyo, Japan, 223-231.