Publications

Joural articles

  1. P. V. Pavlou, S. Filippou, S. Solonos, S. G. Vrachimis, K. Malialis, D. G. Eliades, T. Theocarides, M. M. Polycarpou; Monitoring domestic water consumption: a comparative study of model-based and data-driven end-use disaggregation methods. Journal of Hydroinformatics 2024. [pdf]
  2. K. Malialis, C. G. Panayiotou, M. M. Polycarpou, Nonstationary data stream classification with online active learning and siamese neural networks, Neurocomputing, Volume 512, Nov. 2022, Pages 235-252. [pdf] [code]
  3. K. Malialis, C. G. Panayiotou and M. M. Polycarpou, Online Learning With Adaptive Rebalancing in Nonstationary Environments, in IEEE Transactions on Neural Networks and Learning Systems, 2020. [pdf] [code]
  4. K. Malialis, S. Devlin and D. Kudenko. Distributed Reinforcement Learning for Adaptive and Robust Network Intrusion Response. In Connection Science, Volume 27, Issue 3, July 2015, Pages 234-252. [pdf]
  5. K. Malialis, D. Kudenko. Distributed Response to Network Intrusions Using Multiagent Reinforcement Learning. In Engineering Applications of Artificial Intelligence, Volume 41, May 2015, Pages 270-284. [pdf] (Department's Best Student Paper 2015 Award)

Refereed conference papers

  1. K. Malialis, J. Li, C. G. Panayiotou, M. M. Polycarpou. Incremental learning with concept drift detection and prototype-based embeddings for graph stream classification. In IEEE World Congress on Computational Intelligence (WCCI), 2024. [pdf]
  2. J. Li, K. Malialis, C. G. Panayiotou, M. M. Polycarpou. Unsupervised incremental learning with dual concept drift detection for identifying anomalous sequences. In IEEE World Congress on Computational Intelligence (WCCI), 2024. [pdf]
  3. M. Karapitta, A. Kasis, C. Stylianides, K. Malialis, P. Kolios. Time-varying compartmental models with neural networks for pandemic infection forecasting. In IEEE Engineering in Medicine and Biology Society (EMBC), 2024.
  4. J. Li, K. Malialis, M. M. Polycarpou. Autoencoder-based anomaly detection in streaming data with incremental learning and concept drift adaptation. In IEEE International Joint Conference on Neural Networks (IJCNN), 2023. [pdf]
  5. A. Artelt, K. Malialis, C. G. Panayiotou, M. M. Polycarpou, B. Hammer. Unsupervised unlearning of concept drift with autoencoders. In IEEE Symposium Series on Computational Intelligence, 2023. [pdf]
  6. C. Stylianides, K. Malialis, P. Kolios. A study of data-driven methods for adaptive forecasting of COVID-19 cases. In International Conference on Artificial Neural Networks (ICANN), 2023. [pdf]
  7. S. Filippou, A. Achilleos, S. Z. Zukhraf, C. Laoudias, K. Malialis, M. K. Michael, G. Ellinas. A machine learning approach for detecting GPS location spoofing attacks in autonomous vehicles. In IEEE Vehicular Technology Conference (VTC), 2023. [pdf]
  8. S. Filippou, K. Malialis, C. G. Panayiotou. Improving customer experience in call centers with intelligent customer-agent pairing. In International Conference on Artificial Intelligence Applications and Innovations (AIAI), 2023. [pdf]
  9. P. Valianti, K. Malialis, P. Kolios, G. Ellinas. Multi-agent reinforcement learning for multiple drone interception. In International Conference on Unmanned Aerial Systems (ICUAS), 2023. [pdf]
  10. K. Malialis, M. Roveri, C. Alippi, C. G. Panayiotou, M. M. Polycarpou, "A hybrid active-passive approach to imbalanced nonstationary data stream classification." In IEEE Symposium Series on Computational Intelligence (SSCI), 2022. [pdf]
  11. K. Malialis, D. Papatheodoulou, S. Filippou, C. G. Panayiotou, M. M. Polycarpou, "Data augmentation on-the-fly and active learning in data stream classification." In IEEE Symposium Series on Computational Intelligence (SSCI), 2022. [pdf] [code]
  12. D. Papatheodoulou, P. Pavlou, S. G. Vrachimis, K. Malialis, D. G. Eliades, Theocharides, T. (2022). A Multi-label Time Series Classification Approach for Non-intrusive Water End-Use Monitoring. In: Maglogiannis, I., Iliadis, L., Macintyre, J., Cortez, P. (eds) Artificial Intelligence Applications and Innovations. AIAI 2022. IFIP Advances in Information and Communication Technology, vol 647. Springer, Cham. [pdf]
  13. K. Malialis, C. G. Panayiotou and M. M. Polycarpou. Data-efficient online classification with Siamese networks and active learning. In Proceedings of the World Congress on Computational Intelligence (WCCI), 2020. [pdf]
  14. K. Malialis, C. Panayiotou and M. M. Polycarpou. Queue-based resampling for online class imbalance learning. In Proceedings of the 27th International Conference on Artificial Neural Networks (ICANN), 2018. [pdf] [code]
  15. H. Cai, K. Ren, W. Zhang, K. Malialis, J. Wang, Y. Yu and D. Guo. Real-Time Bidding with Reinforcement Learning in Display Advertising. In Proceedings of the 10th ACM International Conference on Web Search and Data Mining (WSDM), 2017. [pdf] (Acceptance rate 15.8%)
  16. K. Malialis, S. Devlin and D. Kudenko. Resource Abstraction for Reinforcement Learning in Multiagent Congestion Problems. In Proceedings of the 15th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2016. [pdf] [code] (Acceptance rate 24.9%)
  17. K. Malialis, S. Devlin and D. Kudenko. Coordinated Team Learning and Difference Rewards for Distributed Intrusion Response. In Proceedings of the 21st European Conference on Artificial Intelligence (ECAI), 2014. [pdf]
  18. K. Malialis and D. Kudenko. Multiagent Router Throttling: Decentralized Coordinated Response against DDoS Attacks. In Proceedings of the 25th Conference on Innovative Applications of Artificial Intelligence (AAAI / IAAI), 2013. [pdf]

Refereed workshop papers

  1. K. Malialis, J. Wang, G. Brooks, G. Frangou. Feature Selection as a Multiagent Coordination Problem. In AAMAS Workshop on Adaptive and Learning Agents (ALA), 2016. [pdf]
  2. K. Malialis, S. Devlin and D. Kudenko. Intrusion Response Using Difference Rewards for Scalability and Online Learning. In AAMAS Workshop on Adaptive and Learning Agents (ALA), 2014.
  3. K. Malialis and D. Kudenko. Large-Scale DDoS Response Using Cooperative Reinforcement Learning. In 11th European Workshop on Multi-Agent Systems (EUMAS), 2013. [pdf]
  4. K. Malialis and D. Kudenko. Reinforcement Learning of Throttling for DDoS Attack Response. In AAMAS Workshop on Adaptive and Learning Agents (ALA), 2012.

Theses

  1. K. Malialis. Distributed Reinforcement Learning for Network Intrusion Response. PhD thesis, Department of Computer Science, University of York, UK, 2014. [pdf]
  2. K. Malialis. Genetic Algorithms Using Lamarckian Evolution. MEng dissertation, Department of Computer Science, University of York, UK, 2010.