Machine Learning (ML)

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

WEBPAGE:

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. Upcoming Book
    (to appear) Elements of Dimensionality Reduction and Manifold Learning
    Springer Nature, dec, 2023.
  2. Balancing Information with Observation Costs in Deep Reinforcement Learning
    Colin Bellinger, Andriy Drozdyuk, Mark Crowley, and Isaac Tamblyn.
    In Canadian Conference on Artificial Intelligence Canadian Artificial Intelligence Association (CAIAC), Toronto, Ontario, Canada, may, 2022.
  3. Learning from Multiple Independent Advisors in Multi-agent Reinforcement Learning
    In Proceedings of the 22nd International Conference on Autonomous Agents and MultiAgent Systems (AAMAS) International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), London, United Kingdom, sep, 2023.
  4. Affective Search and Rescue Robots to Improve Robot-to-Human Communication in Robot-Assisted Rescue Teams
    Moojan Ghafurian, Mark Crowley, and Kerstin Dautenhahn.
    IEEE Transactions on Affective Computing. 2022.
  5. Scientific Discovery and the Cost of Measurement – Balancing Information and Cost in Reinforcement Learning
    Colin Bellinger, Andriy Drozdyuk, Mark Crowley, and Isaac Tamblyn.
    In 1st Annual AAAI Workshop on AI to Accelerate Science and Engineering (AI2ASE) feb, 2022.
  6. MARLEmpircal
    Investigation of Independent Reinforcement Learning Algorithms in Multi-Agent Environments
    Frontiers in Artificial Intelligence. sep, 2022.
  7. MARLEmpircal
    Investigation of Independent Reinforcement Learning Algorithms in Multi-Agent Environments
    In NeurIPS 2021 Deep Reinforcement Learning Workshop dec, 2021.
  8. VTFR-LBFGS-21
    Vector Transport Free Riemannian LBFGS for Optimization on Symmetric Positive Definite Matrix Manifolds
    Reza Godaz, Benyamin Ghojogh, Reshad Hosseini, Reza Monsefi, Fakhri Karray, and Mark Crowley
    In Asian Conference on Machine Learning (ACML) Virtual, nov, 2021.
  9. A Complementary Approach to Improve WildFire Prediction Systems.
    Sriram Ganapathi Subramanian, and Mark Crowley
    In Neural Information Processing Systems (AI for social good workshop) NeurIPS, 2018.
  10. Amrl
    Active Measure Reinforcement Learning for Observation Cost Minimization: A framework for minimizing measurement costs in reinforcement learning
    Colin Bellinger, Rory Coles, Mark Crowley, and Isaac Tamblyn.
    In Canadian Conference on Artificial Intelligence Springer, 2021.
  11. Offline versus Online Triplet Mining based on Extreme Distances of Histopathology Patches
    Milad Sikaroudi, Benyamin Ghojogh, Amir Safarpoor, Fakhri Karray, Mark Crowley, and H. R. Tizhoosh.
    In 15th International Symposium on Visual Computing (ISCV 2020) Springer International Publishing, (virtual), oct, 2020.
  12. iMondrian
    Isolation Mondrian Forest for Batch and Online Anomaly Detection
    Haoran Ma, Benyamin Ghojogh, Maria N Samad, Dongyu Zheng, and Mark Crowley
    In IEEE International Conference on Systems, Man, and Cybernetics (IEEE-SMC-2020) IEEE SMC, Toronto, Canada (virtual), oct, 2020.
  13. WildfireMLRev
    A review of machine learning applications in wildfire science and management
    Piyush Jain, Sean CP Coogan, Sriram Ganapathi Subramanian, Mark Crowley, Steve Taylor, and Mike D Flannigan.
    Environmental Reviews. 28, (3). Canadian Science Publishing, jul, 2020.
  14. Reinforcement Learning in a Physics-Inspired Semi-Markov Environment
    Colin Bellinger, Rory Coles, Mark Crowley, and Isaac Tamblyn.
    In Canadian Conference on Artificial Intelligence Springer, Ottawa, Canada (virtual), may, 2020.
  15. Semantic Workflows and Machine Learning for the Assessment of Carbon Storage by Urban Trees
    Juan Carrillo, Daniel Garijo, Mark Crowley, Yolanda Gil, and Katherine Borda.
    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, 2019.
  16. TAC-ITS
    Comparison of Deep Learning models for Determining Road Surface Condition from Roadside Camera Images and Weather Data
    J. Carrillo, M. Crowley, G. Pan, and L. Fu.
    In TAC-ITS Canada Joint Conference Halifax, Canada, 2019.
  17. Artificial Counselor System For Stock Investment
    Hadi Nekoei Qachkanloo, Benyamin Ghojogh, Ali Saheb Pasand, and Mark Crowley
    In Innovative Applications of Artificial Intelligence (IAAI-19) AAAI Press., Honolulu, Hawaii, USA, 2019.
  18. ECML
    Compact Representation of a Multi-dimensional Combustion Manifold Using Deep Neural Networks
    Sushrut Bhalla, Matthew Yao, Jean-Pierre Hickey, and Mark Crowley
    In European Conference on Machine Learning (ECML-19) Wurzburg, Germany, 2019.
  19. Integration of Roadside Camera Images and Weather Data for monitoring Winter Road Surface Conditions
    Juan Carrillo, and Mark Crowley
    In Canadian Association of Road Safety Professionals (CARSP) Conference Calgary, Canada, 2019.
  20. Decision Assist for Self-Driving Cars
    Sriram Ganapathi Subramanian, Jaspreet Singh Sambee, Benyamin Ghojogh, and Mark Crowley
    In Canadian Conference on Artificial Intelligence Springer, Toronto, Ontario, Canada, 2018.
  21. Using Spatial Reinforcement Learning to Build Forest Wildfire Dynamics Models From Satellite Images
    Sriram Ganapathi Subramanian, and Mark Crowley
    Frontiers in ICT. 5, (6). Frontiers, apr, 2018.
  22. Big Metadata : Machine Learning on Encrypted Communications
    Jennifer Fernick, and Mark Crowley
    In RSA Conference San Francisco, CA, USA, 2017.
  23. Application of probabilistically-weighted graphs to image-based diagnosis of Alzheimer’s disease using diffusion MRI
    Syeda Maryam, Laura McCrackin, Mark Crowley, Yogesh Rathi, and Oleg Michailovich.
    In Proceedings of SPIE 101324, Medical Imaging 2017 : Computer-Aided Diagnosis International Society for Optics and Photonics, mar, 2017.
  24. PAC Optimal MDP Planning with Application to Invasive Species Management
    Majid Alkaee Taleghan, Thomas G. Dietterich, Mark Crowley, Kim Hall, and H. Jo Albers.
    Journal of Machine Learning Research. 16, 2015.
  25. Using equilibrium policy gradients for spatiotemporal planning in forest ecosystem management
    markcrowley.
    IEEE Transactions on Computers. 63, (1). IEEE computer Society Digital Library. IEEE Computer Society., 2014.
  26. PAC Optimal Planning for Invasive Species Management: Improved Exploration for Reinforcement Learning from Simulator-Defined MDPs
    Thomas G Dietterich, Majid Alkaee Taleghan, and Mark Crowley
    In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-2013) Bellevue, WA, USA, 2013.
  27. Policy Gradient Optimization Using Equilibrium Policies for Spatial Planning Domains
    markcrowley.
    In 13th INFORMS Computing Society Conference Santa Fe, NM, United States, 2013.
  28. phd-thesis
    Equilibrium Policy Gradients for Spatiotemporal Planning
    markcrowley.
    UBC Library, Vancouver, BC, Canada., 2011.