Author Title Type [ Year(Asc)]
Subramanian, S.Ganapathi et al., 2021. Partially Observable Mean Field Reinforcement Learning. In Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS). 3–7 May. London, United Kingdom: International Foundation for Autonomous Agents and Multiagent Systems, pp. 537-545.
Bellinger, C. et al., 2021. Active Measure Reinforcement Learning for Observation Cost Minimization: A framework for minimizing measurement costs in reinforcement learning. In Canadian Conference on Artificial Intelligence. Springer, p. 12.
Allada, A.Krishna et al., 2021. Analysis of Language Embeddings for Classification of Unstructured Pathology Reports. In International Conference of the IEEE Engineering in Medicine and Biology Society. November. IEEE, p. 4.
Sikaroudi, M. et al., 2021. Batch-Incremental Triplet Sampling for Training Triplet Networks Using Bayesian Updating Theorem. In 25th International Conference on Pattern Recognition (ICPR). January. Milan, Italy (virtual): IEEE, p. 7. Available at:
Akgun, S.Alperen et al., 2021. Integrating Affective Expressions into the Search and Rescue Context in order to Improve Non-Verbal Human-Robot Interaction. In Workshop on Exploring Applications for Autonomous Non-Verbal Human-Robot Interactions (HRI). March. Virtual: ACM. Available at:
Sikaroudi, M. et al., 2021. Magnification Generalization for Histopathology Image Embedding. In IEEE International Symposium on Biomedical Imaging (ISBI). April. p. 5.
Crowley, M., 2021. Prediction and Causality: How Can Machine Learning be Used for COVID-19?. 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. July. Waterloo, Canada: Master of Public Service (MPS) Policy and Data Lab, University of Waterloo, p. 6. Available at:
Ghojogh, B., Karray, F. & Crowley, M., 2021. Quantile–Quantile Embedding for Distribution Transformation and Manifold Embedding with Ability to Choose the Embedding Distribution. Machine Learning with Applications (MLWA), 6.
Ghafurian, M. et al., 2021. Recognition of a Robot's Affective Expressions under Conditions with Limited Visibility. In 18th International Conference promoted by the IFIP Technical Committee 13 on Human–Computer Interaction (INTERACT 2021). September. Bari, Italy, p. 22.
mltree.pdf papers2.pdf papers1.pdf Final Published Version
Ma, H. et al., 2020. Isolation Mondrian Forest for Batch and Online Anomaly Detection. IEEE International Conference on Systems, Man, and Cybernetics (SMC) 2020. Available at: arXiv preprint arXiv:2003.03692.
Sikaroudi, M. et al., 2020. Offline versus Online Triplet Mining based on Extreme Distances of Histopathology Patches. In International Conference on Intelligent Systems and Computer Vision (ISCV 2020) . Fez-Morrocco (virtual): IEEE, p. 8. Available at:
Akgun, S.Alperen et al., 2020. Using Emotions to Complement Multi-Modal Human-Robot Interaction in Urban Search and Rescue Scenarios. In 22nd International Conference on Multimodal Interaction (ICMI-2020). October. Utrecht, the Netherlands, p. 9.
Sikaroudi, M. et al., 2020. Supervision and Source Domain Impact on Representation Learning: A Histopathology Case Study. In International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'20). 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'20): IEEE Engineering in Medicine and Biology Society. Available at: