| 2026 | ICDE | EDDI: Explaining Data Drift Using Influence. | Nikolaos Myrtakis, Andrea Castellani, Ioannis Tsamardinos, Vassilis Christophides |
| 2025 | KDD | Data Glitches Discovery using Influence-based Model Explanations. | Nikolaos Myrtakis, Ioannis Tsamardinos, Vassilis Christophides |
| 2024 | DIS | ETIA: Towards an Automated Causal Discovery Pipeline. | Konstantina Biza, Antonios Ntroumpogiannis, Sofia Triantafillou, Ioannis Tsamardinos |
| 2021 | BIBE | Heart Rate Classification Using ECG Signal Processing and Machine Learning Methods. | Maria Papadogiorgaki, Maria Venianaki, Paulos Charonyktakis, Marios Antonakakis, Ioannis Tsamardinos, Michalis E. Zervakis, Vangelis Sakkalis |
| 2021 | ICDE | PROTEUS: Predictive Explanation of Anomalies. | Nikolaos Myrtakis, Ioannis Tsamardinos, Vassilis Christophides |
| 2020 | DIS | Pathway Activity Score Learning for Dimensionality Reduction of Gene Expression Data. | Ioulia Karagiannaki, Yannis Pantazis, Ekaterini Chatzaki, Ioannis Tsamardinos |
| 2020 | EDBT | Putting the Human Back in the AutoML Loop. | Iordanis Xanthopoulos, Ioannis Tsamardinos, Vassilis Christophides, Eric Simon, Alejandro Salinger |
| 2016 | KDD | Towards Robust and Versatile Causal Discovery for Business Applications. | Giorgos Borboudakis, Ioannis Tsamardinos |
| 2016 | PACBB | SCENERY: A Web-Based Application for Network Reconstruction and Visualization of Cytometry Data. | Giorgos Athineou, Giorgos Papoutsoglou, Sofia Triantafillou, Ioannis Basdekis, Vincenzo Lagani, Ioannis Tsamardinos |
| 2016 | UAI | Marginal Causal Consistency in Constraint-based Causal Learning. | Anna Roumpelaki, Giorgos Borboudakis, Sofia Triantafillou, Ioannis Tsamardinos |
| 2016 | UAI | Score-based vs Constraint-based Causal Learning in the Presence of Confounders. | Sofia Triantafillou, Ioannis Tsamardinos |
| 2015 | KDD | Discovering and Exploiting Deterministic Label Relationships in Multi-Label Learning. | Christina Papagiannopoulou, Grigorios Tsoumakas, Ioannis Tsamardinos |
| 2015 | PSB | T-ReCS: Stable Selection of Dynamically Formed Groups of Features with Application to Prediction of Clinical Outcomes. | Grace T. Huang, Ioannis Tsamardinos, Vineet K. Raghu, Naftali Kaminski, Panayiotis V. Benos |
| 2015 | UAI | Bayesian Network Learning with Discrete Case-Control Data. | Giorgos Borboudakis, Ioannis Tsamardinos |
| 2013 | BIBE | A bioinformatics approach for investigating the determinants of Drosha processing. | Nestoras Karathanasis, Ioannis Tsamardinos, Panayiota Poirazi |
| 2013 | UAI | Scoring and Searching over Bayesian Networks with Causal and Associative Priors. | Giorgos Borboudakis, Ioannis Tsamardinos |
| 2012 | BIBE | SVM-based miRNA: MiRNA∗ duplex prediction. | Nestoras Karathanasis, Ioannis Tsamardinos, Angelos P. Armen, Panayiota Poirazi |
| 2012 | ICML | Incorporating Causal Prior Knowledge as Path-Constraints in Bayesian Networks and Maximal Ancestral Graphs. | Giorgos Borboudakis, Ioannis Tsamardinos |
| 2011 | ESANN | A unified approach to estimation and control of the False Discovery Rate in Bayesian network skeleton identification. | Angelos P. Armen, Ioannis Tsamardinos |
| 2011 | ESANN | A constraint-based approach to incorporate prior knowledge in causal models. | Giorgos Borboudakis, Sofia Triantafilou, Vincenzo Lagani, Ioannis Tsamardinos |
| 2008 | AAAI | Bounding the False Discovery Rate in Local Bayesian Network Learning. | Ioannis Tsamardinos, Laura E. Brown |
| 2006 | FlAIRS | Generating Realistic Large Bayesian Networks by Tiling. | Ioannis Tsamardinos, Alexander R. Statnikov, Laura E. Brown, Constantin F. Aliferis |
| 2005 | AAAI | A Comparison of Novel and State-of-the-Art Polynomial Bayesian Network Learning Algorithms. | Laura E. Brown, Ioannis Tsamardinos, Constantin F. Aliferis |
| 2005 | AAAI | Using the GEMS System for Cancer Diagnosis and Biomarker Discovery from Microarray Gene Expression Data. | Alexander R. Statnikov, Ioannis Tsamardinos, Constantin F. Aliferis |
| 2005 | AMIA | A Comparison of Bayesian Network Learning Algorithms from Continuous Data. | Lawrence D. Fu, Ioannis Tsamardinos |
| 2005 | AMIA | Using the | Alexander R. Statnikov, Ioannis Tsamardinos, Constantin F. Aliferis |
| 2004 | ICML | A theoretical characterization of linear SVM-based feature selection. | Douglas P. Hardin, Ioannis Tsamardinos, Constantin F. Aliferis |
| 2003 | AISTATS | Towards Principled Feature Selection: Relevancy, Filters and Wrappers. | Ioannis Tsamardinos, Constantin F. Aliferis |
| 2003 | AMIA | HITON: A Novel Markov Blanket Algorithm for Optimal Variable Selection. | Constantin F. Aliferis, Ioannis Tsamardinos, Alexander R. Statnikov |
| 2003 | FlAIRS | Machine Learning Models for Classification of Lung Cancer and Selection of Genomic Markers Using Array Gene Expression Data. | Constantin F. Aliferis, Ioannis Tsamardinos, Pierre P. Massion, Alexander R. Statnikov, Nafeh Fananapazir, Douglas P. Hardin |
| 2003 | FlAIRS | Algorithms for Large Scale Markov Blanket Discovery. | Ioannis Tsamardinos, Constantin F. Aliferis, Alexander R. Statnikov |
| 2003 | ICDM | Identifying Markov Blankets with Decision Tree Induction. | Lewis J. Frey, Douglas H. Fisher, Ioannis Tsamardinos, Constantin F. Aliferis, Alexander R. Statnikov |
| 2003 | KDD | Time and sample efficient discovery of Markov blankets and direct causal relations. | Ioannis Tsamardinos, Constantin F. Aliferis, Alexander R. Statnikov |
| 1998 | AAAI | Fast Transformation of Temporal Plans for Efficient Execution. | Ioannis Tsamardinos, Nicola Muscettola, Paul H. Morris |
| 1998 | KR | Reformulating Temporal Plans for Efficient Execution. | Nicola Muscettola, Paul H. Morris, Ioannis Tsamardinos |