Machine Learning (ML)

ML is the study of how to build computer programs that can learn to detect patterns from data.

In the broadest terms my research spans the areas of Artificial Intelligence (AI) and Machine Learning (ML) which can be seen highly related, independent, or identical research fields depending on who you are.

My approach to understanding the relationship is summarized in this picture which I use in my courses on the subject.

Our Papers on Machine Learning

  1. Balancing Information with Observation Costs in Deep Reinforcement Learning
    Bellinger, Colin, Drozdyuk, Andriy, Crowley, Mark, and Tamblyn, Isaac
    In Canadian Conference on Artificial Intelligence 2022
  2. Scientific Discovery and the Cost of Measurement – Balancing Information and Cost in Reinforcement Learning
    Bellinger, Colin, Drozdyuk, Andriy, Crowley, Mark, and Tamblyn, Isaac
    In 1st Annual AAAI Workshop on AI to Accelerate Science and Engineering (AI2ASE) 2022
  3. MARLEmpircal
    Investigation of Independent Reinforcement Learning Algorithms in Multi-Agent Environments
    Lee, Ken Ming, Ganapathi Subramanian, Sriram, and Crowley, Mark
    In NeurIPS 2021 Deep Reinforcement Learning Workshop 2021
  4. VTFR-LBFGS-21
    Vector Transport Free Riemannian LBFGS for Optimization on Symmetric Positive Definite Matrix Manifolds
    Godaz, Reza, Ghojogh, Benyamin, Hosseini, Reshad, Monsefi, Reza, Karray, Fakhri, and Crowley, Mark
    In Asian Conference on Machine Learning (ACML) 2021
  5. A Complementary Approach to Improve WildFire Prediction Systems.
    Subramanian, Sriram Ganapathi, and Crowley, Mark
    In Neural Information Processing Systems (AI for social good workshop) 2018
  6. Amrl
    Active Measure Reinforcement Learning for Observation Cost Minimization: A framework for minimizing measurement costs in reinforcement learning
    Bellinger, Colin, Coles, Rory, Crowley, Mark, and Tamblyn, Isaac
    In Canadian Conference on Artificial Intelligence 2021
  7. Offline versus Online Triplet Mining based on Extreme Distances of Histopathology Patches
    Sikaroudi, Milad, Ghojogh, Benyamin, Safarpoor, Amir, Karray, Fakhri, Crowley, Mark, and Tizhoosh, H. R.
    In 15th International Symposium on Visual Computing (ISCV 2020) 2020
  8. iMondrian
    Isolation Mondrian Forest for Batch and Online Anomaly Detection
    Ma, Haoran, Ghojogh, Benyamin, Samad, Maria N, Zheng, Dongyu, and Crowley, Mark
    In IEEE International Conference on Systems, Man, and Cybernetics (IEEE-SMC-2020) 2020
  9. WildfireMLRev
    A review of machine learning applications in wildfire science and management
    Jain, Piyush, Coogan, Sean CP, Ganapathi Subramanian, Sriram, Crowley, Mark, Taylor, Steve, and Flannigan, Mike D
    Environmental Reviews 2020
  10. Reinforcement Learning in a Physics-Inspired Semi-Markov Environment
    Bellinger, Colin, Coles, Rory, Crowley, Mark, and Tamblyn, Isaac
    In Canadian Conference on Artificial Intelligence 2020
  11. Semantic Workflows and Machine Learning for the Assessment of Carbon Storage by Urban Trees
    Carrillo, Juan, Garijo, Daniel, Crowley, Mark, Gil, Yolanda, and Borda, Katherine
    In Third International Workshop on Capturing Scientific Knowledge (Sciknow 2019), Collocated with the tenth International Conference on Knowledge Capture (K-CAP) 2019
  12. Comparison of Deep Learning models for Determining Road Surface Condition from Roadside Camera Images and Weather Data
    Carrillo, J., Crowley, M., Pan, G., and Fu, L.
    In TAC-ITS Canada Joint Conference 2019
  13. Artificial Counselor System For Stock Investment
    Nekoei Qachkanloo, Hadi, Ghojogh, Benyamin, Pasand, Ali Saheb, and Crowley, Mark
    In Innovative Applications of Artificial Intelligence (IAAI-19) 2019
  14. Compact Representation of a Multi-dimensional Combustion Manifold Using Deep Neural Networks
    Bhalla, Sushrut, Yao, Matthew, Hickey, Jean-Pierre, and Crowley, Mark
  15. Integration of Roadside Camera Images and Weather Data for monitoring Winter Road Surface Conditions
    Carrillo, Juan, and Crowley, Mark
    In Canadian Association of Road Safety Professionals (CARSP) Conference 2019
  16. Decision Assist for Self-Driving Cars
    Ganapathi Subramanian, Sriram, Sambee, Jaspreet Singh, Ghojogh, Benyamin, and Crowley, Mark
    In Canadian Conference on Artificial Intelligence 2018
  17. Using Spatial Reinforcement Learning to Build Forest Wildfire Dynamics Models From Satellite Images
    Ganapathi Subramanian, Sriram, and Crowley, Mark
    Frontiers in ICT 2018
  18. Big Metadata : Machine Learning on Encrypted Communications
    Fernick, Jennifer, and Crowley, Mark
    In RSA Conference 2017
  19. Application of probabilistically-weighted graphs to image-based diagnosis of Alzheimer’s disease using diffusion MRI
    Maryam, Syeda, McCrackin, Laura, Crowley, Mark, Rathi, Yogesh, and Michailovich, Oleg
    In Proceedings of SPIE 101324, Medical Imaging 2017 : Computer-Aided Diagnosis 2017
  20. PAC Optimal MDP Planning with Application to Invasive Species Management
    Taleghan, Majid Alkaee, Dietterich, Thomas G., Crowley, Mark, Hall, Kim, and Albers, H. Jo
    Journal of Machine Learning Research 2015
  21. Using equilibrium policy gradients for spatiotemporal planning in forest ecosystem management
    Crowley, Mark
    IEEE Transactions on Computers 2014
  22. PAC Optimal Planning for Invasive Species Management: Improved Exploration for Reinforcement Learning from Simulator-Defined MDPs
    Dietterich, Thomas G, Alkaee Taleghan, Majid, and Crowley, Mark
    In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-2013) 2013
  23. Policy Gradient Optimization Using Equilibrium Policies for Spatial Planning Domains
    Crowley, Mark
    In 13th INFORMS Computing Society Conference 2013
  24. phd-thesis
    Equilibrium Policy Gradients for Spatiotemporal Planning
    Crowley, Mark