publications

Published papers from my lab.

Also see:

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 Confernece on Internet of Things: Systems, Management and Security (IOTSMS) IEEE, Milan, Italy, nov, 2022.
  2. Upcoming Book
    Elements of Dimensionality Reduction and Manifold Learning
    Springer Nature, dec, 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. 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.
  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. 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.
  7. MARLEmpircal
    Investigation of Independent Reinforcement Learning Algorithms in Multi-Agent Environments
    Frontiers in Artificial Intelligence. 2022.
  8. Multi-Advisor-QL
    Multi-Agent Advisor Q-Learning
    Journal of Artificial Intelligence Research (JAIR). 73, may, 2022.

2021

  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. MARLEmpircal
    Investigation of Independent Reinforcement Learning Algorithms in Multi-Agent Environments
    In NeurIPS 2021 Deep Reinforcement Learning Workshop dec, 2021.
  3. TOOL-Gen-LLE
    Generative locally linear embedding: A module for manifold unfolding and visualization
    Software Impacts. 9, (100105). Elsevier, 2021.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. Magnification Generalization for Histopathology Image Embedding
    Milad Sikaroudi, Benyamin Ghojogh, Fakhri Karray, Mark Crowley, and H. R. Tizhoosh.
    In IEEE International Symposium on Biomedical Imaging (ISBI) apr, 2021.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. patent
    Multi-Level Collaborative Control System With Dual Neural Network Planning For Autonomous Vehicle Control In A Noisy Environment
    Zhiyuan Du, Joseph Lull, Rajesh Malhan, Sriram Ganapathi Subramanian, Sushrut Bhalla, Jaspreet Sambee, Mark Crowley, Sebastian Fischmeister, Donghyun Shin, William Melek, Baris Fidan, Ami Woo, and Bismaya Sahoo.
    US Patent Office: #US 11,131,992 B2. sep, 2021.
  14. 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.

2020

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. Deep Multi Agent Reinforcement Learning for Autonomous Driving
    Sushrut Bhalla, Sriram Ganapathi Subramanian, and Mark Crowley
    In Canadian Conference on Artificial Intelligence may, 2020.
  6. Supervision and Source Domain Impact on Representation Learning: A Histopathology Case Study
    Milad Sikaroudi, Amir Safarpoor, Benyamin Ghojogh, Sobhan Shafiei, Mark Crowley, and HR Tizhoosh.
    In International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’20) Montreal, Quebec, Canada (virtual), jul, 2020.
  7. 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.
  8. Fisher Discriminant Triplet and Contrastive Losses for Training Siamese Networks
    Benyamin Ghojogh, Milad Sikaroudi, Sobhan Shafiei, H.R. Tizhoosh, Fakhri Karray, and Mark Crowley
    In IEEE International Joint Conference on Neural Networks (IJCNN) Glasgow, UK (virtual), jul, 2020.
  9. Weighted Fisher Discriminant Analysis in the Input and Feature Spaces
    Benyamin Ghojogh, Milad Sikaroudi, H.R. Tizhoosh, Fakhri Karray, and Mark Crowley
    In International Conference on Image Analysis and Recognition (ICIAR-2020) Springer, Póvoa de Varzim, Portugal (virtual), jun, 2020.
  10. 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.
  11. Theoretical Insights into the Use of Structural Similarity Index In Generative Models and Inferential Autoencoders
    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.
  12. Generalized Subspace Learning by Roweis Discriminant Analysis
    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.
  13. Anomaly Detection and Prototype Selection Using Polyhedron Curvature
    Benyamin Ghojogh, Fakhri Karray, and Mark Crowley
    In Canadian Conference on Artificial Intelligence Ottawa, Canada, 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.

2019

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. Locally Linear Image Structural Embedding for Image Structure Manifold Learning
    Benyamin Ghojogh, Fakhri Karray, and Mark Crowley
    In International Conference on Image Analysis and Recognition (ICIAR-19) Waterloo, Canada, 2019.
  6. Image Structure Subspace Learning Using Structural Similarity Index
    Benyamin Ghojogh, Fakhri Karray, and Mark Crowley
    In International Conference on Image Analysis and Recognition (ICIAR-19) Waterloo, Canada, 2019.
  7. Principal Component Analysis Using Structural Similarity Index for Images
    Benyamin Ghojogh, Fakhri Karray, and Mark Crowley
    In International Conference on Image Analysis and Recognition (ICIAR-19) Waterloo, Canada, 2019.
  8. 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.
  9. 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.
  10. 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.
  11. 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.

2018

  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. 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.
  3. Principal Sample Analysis for Data Reduction
    Benyamin Ghojogh, and Mark Crowley
    In 2018 IEEE International Conference on Big Knowledge (ICBK) Singapore, 2018.
  4. 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.
  5. 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.
  6. 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. Big Metadata : Machine Learning on Encrypted Communications
    Jennifer Fernick, and Mark Crowley
    In RSA Conference San Francisco, CA, USA, 2017.
  3. 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.
  4. AI Education Through Real World Problems
    markcrowley.
    In The Seventh Symposium on Educational Advances in Artificial Intellgience. San Francisco, USA., 2017.

2016

  1. 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.
  2. 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.
  2. Answering Simple Questions About Spatially Spreading Systems
    markcrowley.
    In 2015 Summer Solstice: 7th International Conference on Discrete Models of Complex Systems 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.

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 IJCAI International Joint Conference on Artificial Intelligence 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

  1. 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.

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.
  2. phd-thesis
    Equilibrium Policy Gradients for Spatiotemporal Planning
    markcrowley.
    UBC Library, Vancouver, BC, Canada., 2011.

2010

  1. 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.

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

  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.

2005

  1. msc-thesis
    Shielding Against Conditioning Side-Effects in Graphical Models
    markanthonycrowley.
    UBC Library, Vancouver, BC, Canada., 2005.