[ Author(Desc)] Title Type Year
Bhalla, S. et al., 2019. Compact Representation of a Multi-dimensional Combustion Manifold Using Deep Neural Networks. In European Conference on Machine Learning. Wurzburg, Germany, p. 8.
screen_shot_2019-07-21_at_3.26.52_pm.png ecml_combustion_ml.pdf
Bhalla, S., Subramanian, S.G. & Crowley, M., 2019. Training Cooperative Agents for Multi-Agent Reinforcement Learning. In Proc. of the 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019). Montreal, Canada.
Carrillo, J. et al., 2019. Semantic Workflows and Machine Learning for the Assessment of Carbon Storage by Urban Trees. In Third International Workshop on Capturing Scientific Knowledge (Sciknow 2019), Collocated with the tenth International Conference on Knowledge Capture (K-CAP). Los Angeles, California, USA, p. 6.
Carrillo, J. et al., 2019. Comparison of Deep Learning models for Determining Road Surface Condition from Roadside Camera Images and Weather Data. In The Transportation Association of Canada and Intelligent Transportation Systems Canada Joint Conference (TAC-ITS). Halifax, Canada, p. 16.
Carrillo, J. & Crowley, M., 2019. Integration of Roadside Camera Images and Weather Data for monitoring Winter Road Surface Conditions. In Canadian Association of Road Safety Professionals CARSP Conference. CARSP Conference, Calgary, Alberta. , p. 4 (Won best paper award!). Available at:
Crowley, M., 2015. Answering Simple Questions About Spatially Spreading Systems. In 2015 Summer Solstice: 7th International Conference on Discrete Models of Complex Systems.
Crowley, M., 2013. Policy Gradient Optimization Using Equilibrium Policies for Spatial Planning Domains. In 13th INFORMS Computing Society Conference. Santa Fe, NM, United States.
Crowley, M., 2011. Equilibrium Policy Gradients for Spatiotemporal Planning. University of British Columbia. Available at:
Crowley, M. & Poole, D., 2011. Policy gradient planning for environmental decision making with existing simulators. In 25th AAAI Conference on Artificial Intelligence (AAAI-11). San Francisco, pp. 1323–1330. Available at:
Crowley, M., Nelson, J. & Poole, D., 2009. Seeing the Forest Despite the Trees : Large Scale Spatial-Temporal Decision Making. In Conference on Uncertainty in Artificial Intelligence (UAI09). Montreal, Canada, pp. 126–134. Available at: crowley/papers/uai09-mark-crowley.pdf.
Crowley, M. et al., 2007. Adding Local Constraints to Bayesian Networks. In Advances in Artificial Intelligence. Canadian AI Conference, Montreal, Quebec, Canada, 2007.: Springer Berlin Heidelberg, pp. 344–355. Available at:
Crowley, M., 2005. Shielding Against Conditioning Side-Effects in Graphical Models. University of British Columbia.
Crowley, M., 2004. Evaluating Influence Diagrams. Unpublished Working Paper.
Dietterich, T.G., Taleghan, M.A. & 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). Bellevue, WA, USA, p. 7. Available at:
Ghojogh, B. & Crowley, M., 2019. Instance Ranking and Numerosity Reduction Using Matrix Decompositionand Subspace Learning. In Canadian Conference on Artificial Intelligence. Kingston, ON, Canada: Springer’s Lecture Notes in Artificial Intelligence., p. 12.
Ghojogh, B., Crowley, M. & Karray, F., 2019. Addressing the Mystery of Population Decline of the Rose-Crested Blue Pipit in a Nature Preserve using Data Visualization. ArXiv Preprint. ArXiv: 1903.06671.
Ghojogh, B., Karray, F. & Crowley, M., 2019. Eigenvalue and Generalized Eigenvalue Problems: Tutorial. ArXiv Preprint arXiv:1903.11240.
Ghojogh, B. et al., 2019. Fitting A Mixture Distribution to Data: Tutorial. ArXiv preprint. arXiv:1901.06708.