showcase

A selection of highlighted papers per year

Also see:

selected publications

2023

  1. Dynamic Observation Policies in Observation Cost-Sensitive Reinforcement Learning
    Colin Bellinger, Mark Crowley, and Isaac Tamblyn.
    In Workshop on Advancing Neural Network Training: Computational Efficiency, Scalability, and Resource Optimization (WANT@NeurIPS 2023). New Orleans, USA. 2023.
  2. ChemGymRL
    Demonstrating ChemGymRL: An Interactive Framework for Reinforcement Learning for Digital Chemistry
    Chris Beeler, Sriram Ganapathi Subramanian, Kyle Sprague, Mark Crowley, Colin Bellinger, and Isaac Tamblyn.
    In NeurIPS 2023 AI for Accelerated Materials Discovery (AI4Mat) Workshop. New Orleans, USA. Dec, 2023.
  3. Machine-learning Assisted Swallowing Assessment: a deep learning-based quality improvement tool to screen for post-stroke dysphagia
    Rami Saab, Arjun Balachandar, Hamza Mahdi, Eptehal Nashnoush, Lucas Perri, Ashley Waldron, Alireza Sadeghian, Gordon Rubenfeld, Mark Crowley, Mark I. Boulos, Brian Murray, and Houman Khosravani.
    Frontiers in Neuroscience. 17, Nov, 2023.
  4. ChemGymRL
    ChemGymRL: An Interactive Framework for Reinforcement Learning for Digital Chemistry
    Chris Beeler, Sriram Ganapathi Subramanian, Kyle Sprague, Nouha Chatti, Colin Bellinger, Mitchell Shahen, Nicholas Paquin, Mark Baula, Amanuel Dawit, Zihan Yang, Xinkai Li, Mark Crowley, and Isaac Tamblyn.
    In ICML 2023 Synergy of Scientific and Machine Learning Modeling (SynS&ML) Workshop. Jul, 2023.
  5. Multi-Advisor-QL
    Multi-Agent Advisor Q-Learning
    In International Joint Conference on Artificial Intelligence (IJCAI) : Journal Track. Macao, China. Aug, 2023.
  6. GCRL
    Generative Causal Representation Learning for Out-of-Distribution Motion Forecasting
    In Proceedings of the 40th International Conference on Machine Learning (ICML). PMLR, Honolulu, Hawaii, USA. Jul, 2023.
  7. Textbook
    Elements of Dimensionality Reduction and Manifold Learning
    Springer Nature, Feb, 2023.
  8. Multi-Advisor-MARL
    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.

2022

  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 Conference on Internet of Things: Systems, Management and Security (IOTSMS). IEEE, Milan, Italy. Nov, 2022.
  2. Robot Rescue
    Using Affect as a Communication Modality to Improve Human-Robot Communication in Robot-Assisted Search and Rescue Scenarios
    Sami Alperen Akgun, Moojan Ghafurian, Mark Crowley, and Kerstin Dautenhahn.
    IEEE Transactions on Affective Computing. arXiv, Nov, 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. Multi-Advisor-QL
    Multi-Agent Advisor Q-Learning
    Journal of Artificial Intelligence Research (JAIR). 74, May, 2022.

2021

  1. MARLEmpircal
    Investigation of Independent Reinforcement Learning Algorithms in Multi-Agent Environments
    In NeurIPS 2021 Deep Reinforcement Learning Workshop. Dec, 2021.
  2. Acceleration of Large Margin Metric Learning for Nearest Neighbor Classification Using Triplet Mining and Stratified Sampling
    Parisa Poorheravi, Benyamin Ghojogh, Vincent Gaudet, Fakhri Karray, and Mark Crowley
    Journal of Computational Vision and Imaging Systems. 6, (1). Jan, 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. 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.
  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.

2020

  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. 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.
  3. Distributed Nonlinear Model Predictive Control and Metric Learning for Heterogeneous Vehicle Platooning with Cut-in/Cut-out Maneuvers
    Mohammad Hossein Basiri, Benyamin Ghojogh, Nasser L Azad, Sebastian Fischmeister, Fakhri Karray, and Mark Crowley
    In Proceeding of the 59th IEEE Conference on Decision and Control (CDC-2020). Jeju Island, Korea (virtual). Dec, 2020.

2019

  1. Instance Ranking and Numerosity Reduction Using Matrix Decompositionand Subspace Learning
    Benyamin Ghojogh, and Mark Crowley
    In Canadian Conference on Artificial Intelligence. Springer’s Lecture Notes in Artificial Intelligence., Kingston, ON, Canada. 2019.
  2. 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.
  3. 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.

2018

  1. 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.
  2. 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.

2017

  1. 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.
  2. AI Education Through Real World Problems
    markcrowley.
    In The Seventh Symposium on Educational Advances in Artificial Intellgience.. San Francisco, USA.. 2017.

2016

  1. 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.

2015

  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.

2014

  1. 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.

2013

  1. Allowing a wildfire to burn: Estimating the effect on future fire suppression costs
    Rachel M. Houtman, Claire A. Montgomery, Aaron R. Gagnon, David E. Calkin, Thomas G. Dietterich, Sean McGregor, and Mark Crowley
    International Journal of Wildland Fire. 22, (7). 2013.
  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.
  3. Cyclic causal models with discrete variables: Markov chain equilibrium semantics and sample ordering
    David Poole, and Mark Crowley
    In International Joint Conference on Artificial Intelligence (IJCAI). Beijing, China. 2013.
  4. Policy Gradient Optimization Using Equilibrium Policies for Spatial Planning Domains
    markcrowley.
    In 13th INFORMS Computing Society Conference. Santa Fe, NM, United States. 2013.

2012

    2011

    1. Policy gradient planning for environmental decision making with existing simulators
      Mark Crowley, and David Poole.
      In Proceedings of the Twenty-Fifth Conference on Artificial Intelligence (AAAI). San Francisco. 2011.

    2009

    1. 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.

    2007