Publications

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Author [ Title(Desc)] Type Year
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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: http://www.carsp.ca/research/research-papers/research-papers-search/download-info/integration-of-roadside-camera-images-and-weather-data-for-monitoring-winter-road-surface-conditions/.
Salem, M., Crowley, M. & Fischmeister, S., 2016. Inter-Arrival Curves for Multi-Mode and Online Anomaly Detection. In Euromicro Conference on Real-Time Systems 2016 - Work-in-Progress Proceedings. Toulouse, France.
Salem, M., Crowley, M. & Fischmeister, S., 2016. Inter-Arrival Curves for Multi-Mode and Online Anomaly Detection. In Euromicro Conference on Real-Time Systems 2016 - Work-in-Progress Proceedings. Toulouse, France.
Ma, H. et al., 2020. Isolation Mondrian Forest for Batch and Online Anomaly Detection. IEEE International Conference on Systems, Man, and Cybernetics (SMC) 2020. Available at: arXiv preprint arXiv:2003.03692.
imondrian.pdf
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Subramanian, S.Ganapathi & Crowley, M., 2017. Learning Forest Wildfire Dynamics from Satellite Images Using Reinforcement Learning. In Conference on Reinforcement Learning and Decision Making. Ann Arbor, MI, USA.
Subramanian_Crowley_-_2017_-_Learning_Forest_Wildfire_Dynamics_from_Satellite_Images_Using_Reinforcement_Learning.pdf
Subramanian, S.Ganapthi, Bhalla, S. & Crowley, M., 2019. Learning Multi-Agent Communication with Reinforcement Learning. In Conference on Reinforcement Learning and Decision Making (RLDM-19). Montreal, Canada., p. 4.
Ghojogh, B. & Crowley, M., 2019. Linear and Quadratic Discriminant Analysis: Tutorial. arXiv preprint arXiv:1906.02590.
Ghojogh, B., Karray, F. & Crowley, M., 2019. Locally Linear Image Structural Embedding for Image Structure Manifold Learning. In International Conference on Image Analysis and Recognition (ICIAR-19). Waterloo, Canada: Springer, Cham, pp. 126–138.
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Sikaroudi, M. et al., 2021. Magnification Generalization for Histopathology Image Embedding. In IEEE International Symposium on Biomedical Imaging (ISBI). April. p. 5.
Hall, K. et al., 2012. Managing Invasive Species in a River Network. In Third International Conference on Computational Sustainability. Copenhagen, Denmark. Available at: http://www.cs.ubc.ca/ crowley/papers/compsust2012.pdf.
patent-design.png
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Sikaroudi, M. et al., 2020. Offline versus Online Triplet Mining based on Extreme Distances of Histopathology Patches. In International Conference on Intelligent Systems and Computer Vision (ISCV 2020) . Fez-Morrocco (virtual): IEEE, p. 8. Available at: https://arxiv.org/abs/2007.02200.
2007.02200.pdf
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Taleghan, M.A. et al., 2015. PAC Optimal MDP Planning with Application to Invasive Species Management. Journal of Machine Learning Research, 16, pp.3877–3903. Available at: http://jmlr.org/papers/v16/taleghan15a.html.
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: http://www.aaai.org/ocs/index.php/AAAI/AAAI13/paper/view/6478.
Subramanian, S.Ganapathi et al., 2021. Partially Observable Mean Field Reinforcement Learning. In Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS). 3–7 May. London, United Kingdom: International Foundation for Autonomous Agents and Multiagent Systems, pp. 537-545.
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. & 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: https://www.scopus.com/record/display.uri?eid=2-s2.0-80055051332&origin=inward&txGid=de2006c39235aac9ba20cf0e76073dd9.
Crowley, M., 2021. Prediction and Causality: How Can Machine Learning be Used for COVID-19?. In "What Needs to be done in order to Curb the Spread of Covid-19: Exposure Notification, Legal Considerations, and Statistical Modeling", a Conference on Data and Privacy during a Global Pandemic. July. Waterloo, Canada: Master of Public Service (MPS) Policy and Data Lab, University of Waterloo, p. 6. Available at: https://uwaterloo.ca/master-of-public-service/events/data-and-privacy-during-global-pandemic-conference.
Ghojogh, B., Karray, F. & Crowley, M., 2019. Principal Component Analysis Using Structural Similarity Index for Images. In International Conference on Image Analysis and Recognition (ICIAR-19). Waterloo, Canada: Springer, Cham, pp. 77–88.

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