Publications

2016
[47] Learning to Search for Recognizing Named Entities in Twitter Ioannis Partalas, Cédric Lopez, Nadia Derbas, Ruslan Kalitvianski, In W-NUT, Coling, 2016. [bibtex] [pdf]
[46] Learning Taxonomy Adaptation in Large-scale Classification Rohit Babbar, Ioannis Partalas, Eric Gaussier, Massih-Reza Amini, Cécile Amblard, In Journal of Machine Learning Research, volume 17, 2016. [bibtex] [pdf]
[45] Comparing Named-Entity Recognizers in a Targeted Domain: Handcrafted Rules vs. Machine Learning Ioannis Partalas, Cédric Lopez, Frédérique Segond, In Traitement Automatique des Langues Naturelles, 2016. [bibtex] [pdf]
[44] e-Commerce product classification: our participation at cDiscount 2015 challenge Ioannis Partalas, Georgios Balikas, In CoRR, 2016. [bibtex] [pdf]
2015
[43] Learning semantic models from event logs Vasiliki Sfyrla, Ioannis Partalas, Richard Yann, Sebastian Maunoury, In Proceedings of the Industry Track at the 13th International Conference on Business Process Management 2015, Innsbruck, Austria, September 2015., 2015. [bibtex] [pdf]
[42] Multi-class to Binary reduction of Large-scale classification Problems Bikash Joshi, Massih-Reza Amini, Ioannis Partalas, Liva Ralaivola, Nicolas Usunier, Eric Gaussier, In International Workshop on Big Multi-Target Prediction ECML/PKDD, 2015. [bibtex] [pdf]
[41]On Binary Reduction of Large-Scale Multiclass Classification Problems Bikash Joshi, Massih-Reza Amini, Ioannis Partalas, Liva Ralaivola, Nicolas Usunier, Eric Gaussier, In International Symposium on Intelligent Data Analysis, 2015. [bibtex]
[40]Efficient Model Selection for Regularized Classification by Exploiting Unlabeled Data Georgios Balikas, Ioannis Partalas, Eric Gaussier, Rohit Babbar, Massih-Reza Amini, In International Symposium on Intelligent Data Analysis, 2015. [bibtex]
[39]Sparsification of Linear Models for Large-Scale Text Classification Simon Moura, Ioannis Partalas, Massih-Reza Amini, In Conférence sur l'Apprentissage Automatique, 2015. [bibtex]
[38] An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition George Tsatsaronis, Georgios Balikas, Prodromos Malakasiotis, Ioannis Partalas, Matthias Zschunke, Michael R. Alvers, Dirk Weissenborn, Anastasia Krithara, Sergios Petridis, Dimitris Polychronopoulos, Yannis Almirantis, John Pavlopoulos, Nicolas Baskiotis, Patrick Gallinari, Thierry Artières, Axel Ngonga, Norman Heino, Éric Gaussier, Liliana Barrio-Alvers, Michael Schroeder, Ion Androutsopoulos, Georgios Paliouras, In BMC Bioinformatics, volume 16, 2015. [bibtex] [pdf]
[37] LSHTC: A Benchmark for Large-Scale Text Classification Ioannis Partalas, Aris Kosmopoulos, Nicolas Baskiotis, Thierry Artieres, George Paliouras, Eric Gaussier, Ion Androutsopoulos, Massih-Reza Amini, Patrick Galinari, In CoRR, volume abs/1503.08581, 2015. [bibtex] [pdf]
[36] Special Issue on "Solving complex machine learning problems with ensemble methods" Daniel Hernández-Lobato, Ioannis Katakis, Gonzalo Martínez-Muñoz, Ioannis Partalas, In Neurocomputing, volume 150, 2015. [bibtex] [pdf]
[35] Transfer learning with probabilistic mapping selection Anestis Fachantidis, Ioannis Partalas, Matthew E. Taylor, Ioannis P. Vlahavas, In Adaptive Behaviour, volume 23, 2015. [bibtex] [pdf]
[34]BioASQ: A challenge on large-scale biomedical semantic indexing and question-answering Georgios Balikas, Anastasia Krithara, Ioannis Partalas, Georgios Paliouras, In Multimodal Retrieval in the Medical Domain, Workshop at ECIR 2015, 2015. [bibtex]
2014
[33] Results of the BioASQ Track of the Question Answering Lab at CLEF 2014 Georgios Balikas, Ioannis Partalas, Axel-Cyrille Ngonga Ngomo, Anastasia Krithara, Georgios Paliouras, In Working Notes for CLEF 2014 Conference, Sheffield, UK, September 15-18, 2014., 2014. [bibtex] [pdf]
[32] On Power Law Distributions in Large-scale Taxonomies Rohit Babbar, Cornelia Metzig, Ioannis Partalas, Eric Gaussier, Massih-Reza Amini, In SIGKDD Explorations, 2014. [bibtex] [pdf]
[31] Evaluation Measures for Hierarchical Classification: a unified view and novel approaches Aris Kosmopoulos, Ioannis Partalas, Éric Gaussier, Georgios Paliouras, Ion Androutsopoulos, In Data Mining and Knowledge Discovery, Springer (accepted for publication), 2014. [bibtex] [pdf]
[30] Re-ranking Approach to Classification in Large-scale Power-law Distributed Category Systems Rohit Babbar, Ioannis Partalas, Eric Gaussier, Massih-Reza Amini, In SIGIR (accepted for presentation), 2014. [bibtex] [pdf]
[29] Web-scale classification: web classification in the big data era Ioannis Partalas, Massih-Reza Amini, Ion Androutsopoulos, Thierry Artières, Patrick Gallinari, Éric Gaussier, Georgios Paliouras, In WSDM, 2014. [bibtex] [pdf]
[28] An Autonomous Transfer Learning Algorithm for TD-Learners Anestis Fachantidis, Ioannis Partalas, Matthew E. Taylor, Ioannis Vlahavas, In 8th Hellenic Conference on Artificial Intelligence, 2014. [bibtex] [pdf]
2013
[27] Results of the First BioASQ Workshop Ioannis Partalas, Eric Gaussier, Axel-Cyrille Ngonga Ngomo, In 1st Workshop on Bio-Medical Semantic Indexing and Question Answering, a Post-Conference Workshop of Conference and Labs of the Evaluation Forum 2013 (CLEF 2013), 2013. [bibtex] [pdf]
[26] On Flat versus Hierarchical Classification in Large-Scale Taxonomies Rohit Babbar, Ioannis Partalas, Eric Gaussier, Massih-Reza Amini, In Advances in Neural Information Processing Systems 26 (NIPS), 2013. [bibtex] [pdf]
[25] Maximum-margin Framework for Training Data Synchronization in Large-scale Hierarchical Classification Rohit Babbar, Ioannis Partalas, Eric Gaussier, Massih-Reza Amini, In ICONIP, 2013. [bibtex] [pdf]
[24] Autonomous Selection of Inter-Task Mappings in Transfer Learning (extended abstract) Anestis Fachantidis, Ioannis Partalas, Matthew E. Taylor, Ioannis Vlahavas, In The AAAI 2013 Spring Symposium --- Lifelong Machine Learning, 2013. [bibtex] [pdf]
[23] Transferring task models in Reinforcement Learning agents Anestis Fachantidis, Ioannis Partalas, Grigorios Tsoumakas, Ioannis Vlahavas, In Neurocomputing, volume 107, 2013. [bibtex] [pdf]
[22] Comparative Classifier Evaluation for Web-scale Taxonomies using Power Law (poster) Rohit Babbar, Ioannis Partalas, Cornelia Metzig, Eric Gaussier, Massih-Reza Amini, In Extended Semantic Web Conference, 2013. [bibtex] [pdf]
2012
[21] A Study on Greedy Algorithms for Ensemble Pruning Ioannis Partalas, Grigorios Tsoumakas, Ioannis Vlahavas, Technical report, Aristotle University of Thessaloniki, 2012. [bibtex] [pdf]
[20] On empirical tradeoffs in large scale hierarchical classification Rohit Babbar, Ioannis Partalas, Eric Gaussier, Cecile Amblard, In Proceedings of the 21st ACM international conference on Information and knowledge management, 2012. [bibtex] [pdf]
[19] Adaptive Classifier Selection in Large-Scale Hierarchical Classification Ioannis Partalas, Rohit Babbar, Eric Gaussier, Cecile Amblard, Chapter in Neural Information Processing (Tingwen Huang, Zhigang Zeng, Chuandong Li, ChiSing Leung, eds.), Springer Berlin Heidelberg, volume 7665, 2012. [bibtex] [pdf]
2011
[18] Transferring Models in Hybrid Reinforcement Learning Agents Anestis Fachantidis, Ioannis Partalas, Grigorios Tsoumakas, Ioannis Vlahavas, In 12th Engineering Applications of Neural Networks, 2011. [bibtex] [pdf]
[17] Transfer Learning in Multi-agent Reinforcement Learning Domains Georgios Boutsioukis, Ioannis Partalas, Ioannis Vlahavas, In 9th European Workshop on Reinforcement Learning, 2011. [bibtex] [pdf]
[16] Transferring Evolved Reservoir Features in Reinforcement Learning Tasks Kyriakos Chatzidimitriou, Ioannis Partalas, Pericles Mitkas, Ioannis Vlahavas, In 9th European Workshop on Reinforcement Learning, 2011. [bibtex] [pdf]
[15] Transfer Learning via Multiple Inter-Task Mappings Anestis Fachantidis, Ioannis Partalas, Matthew Taylor, Ioannis Vlahavas, In 9th European Workshop on Reinforcement Learning, 2011. [bibtex] [pdf]
2010
[14] An ensemble uncertainty aware measure for directed hill climbing ensemble pruning Ioannis Partalas, Grigorios Tsoumakas, Ioannis Vlahavas, In Machine Learning, volume 81, 2010. [bibtex] [pdf]
2009
[13] An Ensemble Pruning Primer Grigorios Tsoumakas, Ioannis Partalas, Ioannis Vlahavas, Chapter in Applications of supervised and unsupervised ensemble methods (Oleg Okun, Giorgio Valentini, eds.), Springer Verlag, 2009. [bibtex] [pdf]
[12] Transferring Experience in Reinforcement Learning through Task Decomposition Ioannis Partalas, Grigorios Tsoumakas, Konstantinos Tzevanidis, Ioannis Vlahavas, In AAMAS 09, International Conference on Autonomous Agents and Multiagent Systems, 2009. [bibtex] [pdf]
2008
[11] A Taxonomy and Short Review of Ensemble Selection Grigorios Tsoumakas, Ioannis Partalas, Ioannis Vlahavas, In ECAI 08, Workshop on Supervised and Unsupervised Ensemble Methods and Their Applications, SUEMA, 2008. [bibtex] [pdf]
[10] Pruning an Ensemble of Classifiers via Reinforcement Learning Ioannis Partalas, Grigorios Tsoumakas, Ioannis Vlahavas, In Neurocomputing, Elsevier, volume 72, 2008. [bibtex] [pdf]
[9] A Hybrid Multiagent Reinforcement Learning Approach using Strategies and Fusion Ioannis Partalas, Ioannis Feneris, Ioannis Vlahavas, In International Journal on Artificial Intelligence Tools, volume 17, 2008. [bibtex] [pdf]
[8] Reinforcement Learning with Classifier Selection for Focused Crawling Ioannis Partalas, Georgios Paliouras, Ioannis Vlahavas, In 19th European Conference on Artificial Intelligence, 2008. [bibtex] [pdf]
[7]Reinforcement Learning and Automated Planning: A Survey Ioannis Partalas, Dimitrios Vrakas, Ioannis Vlahavas, Chapter in Advanced Problem Solving Techniques (Dimitrios Vrakas, Ioannis Vlahavas, eds.), IGI Global, 2008. [bibtex]
[6] Greedy Regression Ensemble Selection: Theory and an Application to Water Quality Prediction Ioannis Partalas, Grigorios Tsoumakas, Evaggelos Hatzikos, Ioannis Vlahavas, In Information Sciences, Elsevier, volume 178, 2008. [bibtex] [pdf]
[5] Focused Ensemble Selection: A Diversity-Based Method for Greedy Ensemble Selection Ioannis Partalas, Grigorios Tsoumakas, Ioannis Vlahavas, In 19th European Conference on Artificial Intelligence, 2008. [bibtex] [pdf]
2007
[4] Ensemble Selection for Water Quality Prediction Ioannis Partalas, Evaggelos Hatzikos, Grigorios Tsoumakas, Ioannis Vlahavas, In International Conference on Enginnering Applications of Neural Networks, 2007. [bibtex] [pdf]
[3] Multi-Agent Reinforcement Learning using Strategies and Voting Ioannis Partalas, Ioannis Feneris, Ioannis Vlahavas, In 19th IEEE International Conference on Tools with Artificial Intelligence, 2007. [bibtex] [pdf]
2006
[2]Modern Applications of Machine Learning Georgios Tzanis, Ioannis Katakis, Ioannis Partalas, Ioannis Vlahavas, In Annual SEERC Doctoral Student Conference, 2006. [bibtex]
[1] Ensemble Pruning Using Reinforcement Learning Ioannis Partalas, Grigorios Tsoumakas, Ioannis Katakis, Ioannis Vlahavas, Chapter in Advances in Artificial Intelligence (Grigoris Antoniou, George Potamias, Costas Spyropoulos, Dimitris Plexousakis, eds.), Springer Berlin Heidelberg, volume 3955, 2006. [bibtex] [pdf] [doi]