Below, you may find a list of representative publications. For an up-to-date list consult Google Scholar.
Interpretable Machine Learning
- Nikolaos M. Freris, Ahmad Ajalloeian, Michalis Vlachos: Interpretable Embedding and Visualization of Compressed Data. ACM Trans. Knowl. Discov. Data 17(2): 22:1-22:22 (2023)
- Johannes Schneider, Michalis Vlachos: Explaining classifiers by constructing familiar concepts. Mach. Learn. 112(11): 4167-4200 (2023)
- Ahmad Ajalloeian, Seyed-Mohsen Moosavi-Dezfooli, Michalis Vlachos, Pascal Frossard: Sparse Attacks for Manipulating Explanations in Deep Neural Network Models. ICDM 2023: 918-923
- NM Freris, A. Ajalloeian, Michalis Vlachos: Interpretable Embedding and Visualization of Compressed Data. ACM Transaction on Knowledge Discovery from Data,TKDD (2022)
- Ahmad Ajalloeian, Seyed Mohsen Moosavi-Dezfooli, Michalis Vlachos, Pascal Frossard: On smoothed explanations: Quality and Robustness, CIKM (2022)
- Johannes Schneider, Christian Meske, Michalis Vlachos:
- Deceptive AI Explanations: Creation and Detection. ICAART (2) 2022: 44-55 [Best Paper Award]
- Johannes Schneider, Michalis Vlachos: Explaining Neural Networks by Decoding Layer Activations. IDA 2021: 63-75
- Johannes Schneider, Michalis Vlachos: Personalization of Deep Learning. Data Science-Analytics and Applications: 89-96 (2021)
- Johannes Schneider, Joshua Handali, Michalis Vlachos, Christian Meske: Deceptive AI Explanations: Creation and Detection. CoRR abs/2001.07641 (2020)
- Johannes Schneider, Michalis Vlachos: Reflective-Net: Learning from Explanations. CoRR abs/2011.13986 (2020)
- Nikolaos M. Freris, Michalis Vlachos, Ahmad Ajalloeian: An Interpretable Data Embedding under Uncertain Distance Information. ICDM 2020: 1022-1027
- Michail Vlachos, Vassilios G. Vassiliadis, Reinhard Heckel, A. Labbi: Toward interpretable predictive models in B2B recommender systems. IBM J. Res. Dev. 60(5/6): 11:1-11:12 (2016)
Recommender Systems
- Michalis Vlachos, Mircea Lungu, Yash Raj Shrestha, Johannes-Rudolf David:
- Large Language Models for Difficulty Estimation of Foreign Language Content with Application to Language Learning. CoRR abs/2309.05142 (2023)
- Ahmad Ajalloeian, Michalis Vlachos, Johannes Schneider, Alexis Steinmann: A case study in educational recommender systems: Recommending Music Partitures at Tomplay, CIKM (2022)
- Michail Vlachos, Celestine Dünner, Reinhard Heckel, Vassilios G. Vassiliadis, Thomas P. Parnell, Kubilay Atasu: Addressing Interpretability and Cold-Start in Matrix Factorization for Recommender Systems.IEEE Trans. Knowl. Data Eng. 31(7): 1253-1266 (2019)
- Francesco Fusco, Michalis Vlachos, Vasileios Vasileiadis, Kathrin Wardatzky, Johannes Schneider: RecoNet: An Interpretable Neural Architecture for Recommender Systems.IJCAI 2019: 2343-2349
- Reinhard Heckel, Michail Vlachos, Thomas P. Parnell, Celestine Dünner: Scalable and Interpretable Product Recommendations via Overlapping Co-Clustering.ICDE 2017: 1033-1044
- Michail Vlachos, Francesco Fusco, Charalampos Mavroforakis, Anastasios Kyrillidis, Vassilios G. Vassiliadis: Improving Co-Cluster Quality with Application to Product Recommendations.CIKM 2014: 679-688
Big Data & Information Retrieval
- Anastasios Zouzias, Michail Vlachos: Very-Low Random Projection Maps.EDBT 2018: 421-424
- Johannes Schneider, Michail Vlachos:Scalable density-based clustering with quality guarantees using random projections.Data Min. Knowl. Discov. 31(4): 972-1005 (2017)
- Kubilay Atasu, Thomas P. Parnell, Celestine Dünner, Manolis Sifalakis, Haralampos Pozidis, Vasileios Vasileiadis, Michail Vlachos, Cesar Berrospi, Abdel Labbi: Linear-complexity relaxed word Mover’s distance with GPU acceleration.IEEE BigData 2017: 889-896
- Michail Vlachos, Nikolaos M. Freris, Anastasios Kyrillidis: Compressive mining: fast and optimal data mining in the compressed domain.VLDB J. 24(1): 1-24 (2015)
- Johannes Schneider, Michail Vlachos: On Randomly Projected Hierarchical Clustering with Guarantees.SDM 2014: 407-415
- Reinhard Heckel, Michail Vlachos: Private and Right-Protected Big Data Publication: An Analysis. SDM 2017: 660-668
- Spyros I. Zoumpoulis, Michail Vlachos
, Nikolaos M. Freris, Claudio Lucchese: Right-Protected Data Publishing with Provable Distance-Based Mining.IEEE Trans. Knowl. Data Eng. 26(8): 2014-2028 (2014) - Suleyman Serdar Kozat, Michail Vlachos, Claudio Lucchese, Helga Van Herle, Philip S. Yu: Embedding and Retrieving Private Metadata in Electrocardiograms.J. Medical Syst. 33(4): 241-259 (2009)
- Anastasios Zouzias, Michail Vlachos, Vagelis Hristidis: Templated Search over Relational Databases.CIKM 2014: 21-30
- Vagelis Hristidis, Oscar Valdivia, Michail Vlachos, Philip S. Yu: Information discovery across multiple streams.Inf. Sci. 179(19): 3268-3285 (2009)
Patents
A large portion of my research on recommender systems, data analytics and machine learning has been granted a patent by the US PTO (20+ patents).
See here.
