School of Informatics AUTh
A. Lagopoulos, A. Fachantidis, G. Tsoumakas (2017) Multi-Label Modality Classification for Figures in Biomedical Literature, 30th IEEE International Symposium on Computer-Based Medical Systems
A. Fachantidis, A. Tsiaras, G. Tsoumakas, I. Vlahavas, “Segmento: An R-based Visualization-rich System for Customer Segmentation and Targeting”, SETN 2016 9th Hellenic Conference on Artificial Intelligence, (accepted for presentation), Thessaloniki, Greece, 18-21 May 2016
E. Rigas, S. Ramchurn, N. Bassiliades, “Algorithms for Electric Vehicle Scheduling in Mobility-on-Demand Schemes”, Proc. 2015 IEEE 18th International Conference on Intelligent Transportation Systems (ITSC), Las Palmas de Gran Canaria, Spain, 15-18 Sep 2015, pp. 1339-1344.
A. Fachantidis, I. Partalas, M. Taylor, I. Vlahavas, “An Autonomous Transfer Learning Algorithm for TD-Learners”, Proc. 8th Hellenic Conference on Artificial Intelligence (SETN 2014), (in press), Ioannina, Greece, 2014.
Y. Zhan, A. Fachantidis, I. Vlahavas, M. Taylor, “Agents Teaching Humans in Reinforcement Learning Tasks”, Adaptive Learning Agents 2014, (in press), Paris, France, 2014.
P. Bailis, A. Fachantidis, I. Vlahavas, “Learning to play Monopoly: A Reinforcement Learning approach”, AISB 2014: AI & GAMES, (in press), London, U.K., 2014.
A. Fachantidis, I. Partalas, M. Taylor, I. Vlahavas, “Autonomous Selection of Inter-Task Mappings in Transfer Learning”, 2013 AAAI Spring Symposium Series, AAAI, Stanford, U.S.A., 2013.
A. Fachantidis, A. Di Nuovo, A. Cangelosi, I. Vlahavas, “Model-based reinforcement learning for humanoids: A study on forming rewards with the iCub platform”, Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB), 2013 IEEE SSCI, IEEE, pp. 87-93, Singapore, 2013.
A. Fachantidis, I. Partalas, M. Taylor, I. Vlahavas, “Transfer Learning via Multiple Inter-Task Mappings”, Recent Advances in Reinforcement Learning, Springer Berlin Heidelberg, pp. 225-236, 2012.
A. Fachantidis, I. Partalas, G. Tsoumakas, I. Vlahavas, “Transferring Models in Hybrid Reinforcement Learning Agents”, Proc. Engineering Applications of Neural Networks, Springer Berlin Heidelberg, pp. 162-171, 2011.
