Publication List by Topic

List of all Publications Grouped by Selected Reseach Topic

Jump to Topic: anomaly-detection ~ bayesian-networks ~ causality ~ computational-sustainability ~ deep-learning ~ digital-chemistry ~ dimensionality-reduction ~ education-research ~ ensemble-methods ~ fuzzy-logic ~ game-theory ~ human-robot-interaction ~ image-processing ~ marl ~ mean-field-theory ~ multi-agent-systems ~ pac-learning ~ probabilistic-interence ~ reinforcement-learning

Note that papers will show up in multiple topics.

anomaly-detection

  1. 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.
  2. Anomaly Detection and Prototype Selection Using Polyhedron Curvature
    Benyamin Ghojogh, Fakhri Karray, and Mark Crowley
    In Canadian Conference on Artificial Intelligence Ottawa, Canada, 2020.
  3. Inter-Arrival Curves for Multi-Mode and Online Anomaly Detection
    Mahmoud Salem, Mark Crowley, and Sebastian Fischmeister.
    In Euromicro Conference on Real-Time Systems 2016 - Work-in-Progress Proceedings Toulouse, France, 2016.
  4. Anomaly Detection Using Inter-Arrival Curves for Real-time Systems
    Mahmoud Salem, Mark Crowley, and Sebastian Fischmeister.
    In 2016 28th Euromicro Conference on Real-Time Systems Toulouse, France, jul, 2016.

bayesian-networks

  1. Adding Local Constraints to Bayesian Networks
    Mark Crowley, Brent Boerlage, David Poole, Ziad Kobti, and Dan Wu.
    In Advances in Artificial Intelligence Springer Berlin Heidelberg, Montreal, Quebec, Canada, 2007.
  2. msc-thesis
    Shielding Against Conditioning Side-Effects in Graphical Models
    markanthonycrowley.
    UBC Library, Vancouver, BC, Canada., 2005.

causality

  1. Prediction and Causality: How Can Machine Learning be Used for COVID-19?
    markcrowley.
    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 Waterloo, Canada, jul, 2021.

computational-sustainability

  1. 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.
  2. 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.
  3. MCTS+A3C
    Combining MCTS and A3C for prediction of spatially spreading processes in forest wildfire settings
    Sriram Ganapathi Subramanian, and Mark Crowley
    In Canadian Conference on Artificial Intelligence Toronto, Ontario, Canada, 2018.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. Policy Gradient Optimization Using Equilibrium Policies for Spatial Planning Domains
    markcrowley.
    In 13th INFORMS Computing Society Conference Santa Fe, NM, United States, 2013.
  9. Managing Invasive Species in a River Network
    Kim Hall, Majid Alkaee Taleghan, Heidi J. Albers, Thomas Dietterich, and Mark Crowley
    In Third International Conference on Computational Sustainability Copenhagen, Denmark, 2012.
  10. phd-thesis
    Equilibrium Policy Gradients for Spatiotemporal Planning
    markcrowley.
    UBC Library, Vancouver, BC, Canada., 2011.
  11. Seeing the Forest Despite the Trees : Large Scale Spatial-Temporal Decision Making
    Mark Crowley, John Nelson, and David Poole.
    In Conference on Uncertainty in Artificial Intelligence (UAI09) Montreal, Canada, 2009.

deep-learning

  1. IOTSMS
    Aggressive Driver Behavior Detection using Parallel Convolutional Neural Networks on Simulated and Real Driving Data
    Zehra Camlica, Jim Quesenberry, Daniel Carballo, and Mark Crowley
    In 9th International Confernece on Internet of Things: Systems, Management and Security (IOTSMS) IEEE, Milan, Italy, nov, 2022.
  2. MASc-Thesis
    Histopathology Image Analysis and NLP for Digital Pathology.
    aishwaryaalladaallada.
    UWSpace, Waterloo, Canada, aug, 2021.
  3. Multi-Advisor-QL
    Multi-Agent Advisor Q-Learning
    Journal of Artificial Intelligence Research (JAIR). 73, may, 2022.
  4. NLP-DigiPath
    Analysis of Language Embeddings for Classification of Unstructured Pathology Reports
    Aishwarya Krishna Allada, Yuanxin Wang, Veni Jindal, Morteza Babaie, H.R. Tizhoosh, and Mark Crowley
    In International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) IEEE, nov, 2021.
  5. 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.
  6. Backprojection for Training Feedforward Neural Networks in the Input and Feature Spaces
    Benyamin Ghojogh, Fakhri Karray, and Mark Crowley
    In International Conference on Image Analysis and Recognition (ICIAR-2020) Springer, Póvoa de Varzim, Portugal (virtual), jun, 2020.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.

digital-chemistry

  1. SubWorld
    Dynamic programming with partial information to overcome navigational uncertainty in a nautical environment
    Chris Beeler, Xinkai Li, Mark Crowley, Maia Fraser, and Isaac Tamblyn.
    IEEE Intelligent Systems. IEEE, 2022.
  2. 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.
  3. 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.
  4. 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.

dimensionality-reduction

  1. QQE
    Quantile–Quantile Embedding for distribution transformation and manifold embedding with ability to choose the embedding distribution
    Benyamin Ghojogh, Fakhri Karray, and Mark Crowley
    Machine Learning with Applications (MLWA). 6, 2021.
  2. Upcoming Book
    Elements of Dimensionality Reduction and Manifold Learning
    Springer Nature, dec, 2022.
  3. Theoretical Connection between Locally Linear Embedding, Factor Analysis, and Probabilistic PCA
    In Canadian Conference on Artificial Intelligence Canadian Conference on Artificial Intelligence (CAIAC), may, 2022.
  4. Phd Thesis
    Data Reduction Algorithms in Machine Learning and Data Science
    benyaminghojogh.
    feb, 2021.
  5. QQE
    Quantile–Quantile Embedding for distribution transformation and manifold embedding with ability to choose the embedding distribution
    Benyamin Ghojogh, Fakhri Karray, and Mark Crowley
    Machine Learning with Applications (MLWA). 6, 2021.

education-research

  1. Blue Sky Ideas in Artificial Intelligence Education from the EAAI 2017 New and Future AI Educator Program
    Eric Eaton, Sven Koenig, Claudia Schulz, Francesco Maurelli, John Lee, Joshua Eckroth, Mark Crowley, Richard G Freedman, Rogelio E Cardona-Rivera, Tiago Machado, and Tom Williams.
    ACM, New York, NY, USA, feb, 2018.
  2. Circuits and logic in the lab : Toward a coherent picture of computation
    Elizabeth Patitsas, Kimberly Voll, Mark Crowley, and Steven Wolfman.
    In 15th Western Canadian Conference on Computing Education Kelowna, BC, Canada, 2010.

ensemble-methods

    fuzzy-logic

    1. 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.

    game-theory

    1. Phd Thesis
      Multi-Agent Reinforcement Learning in Large Complex Environments
      sriramganapathisubramanian.
      UWSpace, jun, 2022.
    2. Mean Field MARL
      Decentralized Mean Field Games
      Sriram Ganapathi Subramanian, Matthew Taylor, Mark Crowley, and Pascal Poupart.
      In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-2022) Virtual, feb, 2022.
    3. PO-MFRL
      Partially Observable Mean Field Reinforcement Learning
      Sriram Ganapathi Subramanian, Matthew Taylor, Mark Crowley, and Pascal Poupart.
      In Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS) International Foundation for Autonomous Agents and Multiagent Systems, London, United Kingdom, may, 2021.

    human-robot-interaction

    1. Affective Search and Rescue Robots to Improve Robot-to-Human Communication in Robot-Assisted Rescue Teams
      Sami Alperen Akgun, Moojan Ghafurian, Mark Crowley, and Kerstin Dautenhahn.
      IEEE Transactions on Affective Computing. 2022.
    2. Integrating Affective Expressions into the Search and Rescue Context in order to Improve Non-Verbal Human-Robot Interaction
      Sami Alperen Akgun, Moojan Ghafurian, Mark Crowley, and Kerstin Dautenhahn.
      In Workshop on Exploring Applications for Autonomous Non-Verbal Human-Robot Interactions (HRI) ACM, Virtual, mar, 2021.
    3. Using Emotions to Complement Multi-Modal Human-Robot Interaction in Urban Search and Rescue Scenarios
      Sami Alperen Akgun, Moojan Ghafurian, Mark Crowley, and Kerstin Dautenhahn.
      In Proceedings of the 2020 International Conference on Multimodal Interaction (ICMI) Association for Computing Machinery, Utrecht, the Netherlands (virtual), 2020.
    4. Emotion Modelling for Robot to Human Communication in Search and Rescue Contexts
      Sami Alperen Akgun, Moojan Ghafurian, Mark Crowley, and Kerstin Dautenhahn.
      IEEE Transactions on Affective Computing (IEEE TAC). 2021.
    5. Recognition of a Robot’s Affective Expressions under Conditions with Limited Visibility
      Moojan Ghafurian, Sami Alperen Akgun, Mark Crowley, and Kerstin Dautenhahn.
      In 18th International Conference promoted by the IFIP Technical Committee 13 on Human–Computer Interaction (INTERACT 2021) Bari, Italy, sep, 2021.

    image-processing

    1. QQE
      Quantile–Quantile Embedding for distribution transformation and manifold embedding with ability to choose the embedding distribution
      Benyamin Ghojogh, Fakhri Karray, and Mark Crowley
      Machine Learning with Applications (MLWA). 6, 2021.
    2. MASc-Thesis
      Histopathology Image Analysis and NLP for Digital Pathology.
      aishwaryaalladaallada.
      UWSpace, Waterloo, Canada, aug, 2021.
    3. NLP-DigiPath
      Analysis of Language Embeddings for Classification of Unstructured Pathology Reports
      Aishwarya Krishna Allada, Yuanxin Wang, Veni Jindal, Morteza Babaie, H.R. Tizhoosh, and Mark Crowley
      In International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) IEEE, nov, 2021.
    4. Batch-Incremental Triplet Sampling for Training Triplet Networks Using Bayesian Updating Theorem
      Milad Sikaroudi, Benyamin Ghojogh, Fakhri Karray, Mark Crowley, and H. R. Tizhoosh.
      In 25th International Conference on Pattern Recognition (ICPR) IEEE, Milan, Italy (virtual), jan, 2021.
    5. QQE
      Quantile–Quantile Embedding for distribution transformation and manifold embedding with ability to choose the embedding distribution
      Benyamin Ghojogh, Fakhri Karray, and Mark Crowley
      Machine Learning with Applications (MLWA). 6, 2021.
    6. MCTS+A3C
      Combining MCTS and A3C for prediction of spatially spreading processes in forest wildfire settings
      Sriram Ganapathi Subramanian, and Mark Crowley
      In Canadian Conference on Artificial Intelligence Toronto, Ontario, Canada, 2018.
    7. 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.
    8. Learning Forest Wildfire Dynamics from Satellite Images Using Reinforcement Learning
      Sriram Ganapathi Subramanian, and Mark Crowley
      In Conference on Reinforcement Learning and Decision Making Ann Arbor, MI, USA., 2017.
    9. 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.

    marl

    1. Multi Type Mean Field Reinforcement Learning
      Sriram Ganapathi Subramanian, Pascal Poupart, Matthew Taylor, and Nidhi Hegde.
      In Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS) International Foundation for Autonomous Agents and Multiagent Systems, London, United Kingdom, 2020.
    2. Phd Thesis
      Multi-Agent Reinforcement Learning in Large Complex Environments
      sriramganapathisubramanian.
      UWSpace, jun, 2022.
    3. Mean Field MARL
      Decentralized Mean Field Games
      Sriram Ganapathi Subramanian, Matthew Taylor, Mark Crowley, and Pascal Poupart.
      In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-2022) Virtual, feb, 2022.
    4. MARLEmpircal
      Investigation of Independent Reinforcement Learning Algorithms in Multi-Agent Environments
      Frontiers in Artificial Intelligence. 2022.
    5. MARLEmpircal
      Investigation of Independent Reinforcement Learning Algorithms in Multi-Agent Environments
      In NeurIPS 2021 Deep Reinforcement Learning Workshop dec, 2021.
    6. PO-MFRL
      Partially Observable Mean Field Reinforcement Learning
      Sriram Ganapathi Subramanian, Matthew Taylor, Mark Crowley, and Pascal Poupart.
      In Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS) International Foundation for Autonomous Agents and Multiagent Systems, London, United Kingdom, may, 2021.
    7. Deep Multi Agent Reinforcement Learning for Autonomous Driving
      Sushrut Bhalla, Sriram Ganapathi Subramanian, and Mark Crowley
      In Canadian Conference on Artificial Intelligence may, 2020.
    8. Learning Multi-Agent Communication with Reinforcement Learning
      Sushrut Bhalla, Sriram Ganapathi Subramanian, and Mark Crowley
      In Conference on Reinforcement Learning and Decision Making (RLDM-19) Montreal, Canada, 2019.
    9. Training Cooperative Agents for Multi-Agent Reinforcement Learning
      Sushrut Bhalla, Sriram Ganapathi Subramanian, and Mark Crowley
      In Proc. of the 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019) Montreal, Canada, 2019.

    mean-field-theory

    1. Mean Field MARL
      Decentralized Mean Field Games
      Sriram Ganapathi Subramanian, Matthew Taylor, Mark Crowley, and Pascal Poupart.
      In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-2022) Virtual, feb, 2022.
    2. PO-MFRL
      Partially Observable Mean Field Reinforcement Learning
      Sriram Ganapathi Subramanian, Matthew Taylor, Mark Crowley, and Pascal Poupart.
      In Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS) International Foundation for Autonomous Agents and Multiagent Systems, London, United Kingdom, may, 2021.

    multi-agent-systems

    1. Multi Type Mean Field Reinforcement Learning
      Sriram Ganapathi Subramanian, Pascal Poupart, Matthew Taylor, and Nidhi Hegde.
      In Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS) International Foundation for Autonomous Agents and Multiagent Systems, London, United Kingdom, 2020.
    2. 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.
    3. Mean Field MARL
      Decentralized Mean Field Games
      Sriram Ganapathi Subramanian, Matthew Taylor, Mark Crowley, and Pascal Poupart.
      In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-2022) Virtual, feb, 2022.
    4. Multi-Advisor-QL
      Multi-Agent Advisor Q-Learning
      Journal of Artificial Intelligence Research (JAIR). 73, may, 2022.

    pac-learning

    1. 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.
    2. 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.

    probabilistic-interence

      reinforcement-learning

      1. Multi Type Mean Field Reinforcement Learning
        Sriram Ganapathi Subramanian, Pascal Poupart, Matthew Taylor, and Nidhi Hegde.
        In Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS) International Foundation for Autonomous Agents and Multiagent Systems, London, United Kingdom, 2020.
      2. Phd Thesis
        Multi-Agent Reinforcement Learning in Large Complex Environments
        sriramganapathisubramanian.
        UWSpace, jun, 2022.
      3. 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.
      4. 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.
      5. SubWorld
        Dynamic programming with partial information to overcome navigational uncertainty in a nautical environment
        Chris Beeler, Xinkai Li, Mark Crowley, Maia Fraser, and Isaac Tamblyn.
        IEEE Intelligent Systems. IEEE, 2022.
      6. 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.
      7. Mean Field MARL
        Decentralized Mean Field Games
        Sriram Ganapathi Subramanian, Matthew Taylor, Mark Crowley, and Pascal Poupart.
        In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-2022) Virtual, feb, 2022.
      8. MARLEmpircal
        Investigation of Independent Reinforcement Learning Algorithms in Multi-Agent Environments
        Frontiers in Artificial Intelligence. 2022.
      9. MARLEmpircal
        Investigation of Independent Reinforcement Learning Algorithms in Multi-Agent Environments
        In NeurIPS 2021 Deep Reinforcement Learning Workshop dec, 2021.
      10. Multi-Advisor-QL
        Multi-Agent Advisor Q-Learning
        Journal of Artificial Intelligence Research (JAIR). 73, may, 2022.
      11. Prediction and Causality: How Can Machine Learning be Used for COVID-19?
        markcrowley.
        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 Waterloo, Canada, jul, 2021.
      12. 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.
      13. PO-MFRL
        Partially Observable Mean Field Reinforcement Learning
        Sriram Ganapathi Subramanian, Matthew Taylor, Mark Crowley, and Pascal Poupart.
        In Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS) International Foundation for Autonomous Agents and Multiagent Systems, London, United Kingdom, may, 2021.
      14. 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.
      15. Deep Multi Agent Reinforcement Learning for Autonomous Driving
        Sushrut Bhalla, Sriram Ganapathi Subramanian, and Mark Crowley
        In Canadian Conference on Artificial Intelligence may, 2020.
      16. Learning Multi-Agent Communication with Reinforcement Learning
        Sushrut Bhalla, Sriram Ganapathi Subramanian, and Mark Crowley
        In Conference on Reinforcement Learning and Decision Making (RLDM-19) Montreal, Canada, 2019.
      17. Training Cooperative Agents for Multi-Agent Reinforcement Learning
        Sushrut Bhalla, Sriram Ganapathi Subramanian, and Mark Crowley
        In Proc. of the 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019) Montreal, Canada, 2019.
      18. Learning Forest Wildfire Dynamics from Satellite Images Using Reinforcement Learning
        Sriram Ganapathi Subramanian, and Mark Crowley
        In Conference on Reinforcement Learning and Decision Making Ann Arbor, MI, USA., 2017.
      19. Policy Gradient Optimization Using Equilibrium Policies for Spatial Planning Domains
        markcrowley.
        In 13th INFORMS Computing Society Conference Santa Fe, NM, United States, 2013.
      20. phd-thesis
        Equilibrium Policy Gradients for Spatiotemporal Planning
        markcrowley.
        UBC Library, Vancouver, BC, Canada., 2011.