Fast Transformation of Temporal Plans for Efficient Execution. - Ioannis Tsamardinos; Nicola Muscettola; Paul H. Morris (AAAI/IAAI, 1998)

Reformulating Temporal Plans for Efficient Execution. - Nicola Muscettola; Paul H. Morris; Ioannis Tsamardinos (KR, 1998)

The potential for the evolution of co-operation among web agents. - Cristina Bicchieri; Martha E. Pollack; Carlo Rovelli; Ioannis Tsamardinos (Int. J. Hum. Comput. Stud., 1998)

Merging Plans with Quantitative Temporal Constraints, Temporally Extended Actions, and Conditional Branches. - Ioannis Tsamardinos; Martha E. Pollack; John F. Horty (AIPS, 2000)

Execution-Time Plan Management for a Cognitive Orthotic System. - Martha E. Pollack; Colleen E. McCarthy; Sailesh Ramakrishnan; Ioannis Tsamardinos (Advances in Plan-Based Control of Robotic Agents, 2001)

A Scheme for Integrating e-Services in Establishing Virtual Enterprises. - Alan Berfield; Panos K. Chrysanthis; Ioannis Tsamardinos; Martha E. Pollack; Sujata Banerjee (RIDE, 2002)

A Probabilistic Approach to Robust Execution of Temporal Plans with Uncertainty. - Ioannis Tsamardinos (SETN, 2002)

Towards Principled Feature Selection: Relevancy, Filters and Wrappers. - Ioannis Tsamardinos; Constantin F. Aliferis (AISTATS, 2003)

HITON: A Novel Markov Blanket Algorithm for Optimal Variable Selection. - Constantin F. Aliferis; Ioannis Tsamardinos; Alexander R. Statnikov (AMIA, 2003)

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 (FLAIRS, 2003)

Algorithms for Large Scale Markov Blanket Discovery. - Ioannis Tsamardinos; Constantin F. Aliferis; Alexander R. Statnikov (FLAIRS, 2003)

Identifying Markov Blankets with Decision Tree Induction. - Lewis J. Frey; Douglas H. Fisher; Ioannis Tsamardinos; Constantin F. Aliferis; Alexander R. Statnikov (ICDM, 2003)

Time and sample efficient discovery of Markov blankets and direct causal relations. - Ioannis Tsamardinos; Constantin F. Aliferis; Alexander R. Statnikov (KDD, 2003)

Why Classification Models Using Array Gene Expression Data Perform So Well: A Preliminary Investigation of Explanatory Factors. - Constantin F. Aliferis; Ioannis Tsamardinos; Pierre P. Massion; Alexander R. Statnikov; Douglas P. Hardin (METMBS, 2003)

Causal Explorer: A Causal Probabilistic Network Learning Toolkit for Biomedical Discovery. - Constantin F. Aliferis; Ioannis Tsamardinos; Alexander R. Statnikov; Laura E. Brown (METMBS, 2003)

Efficient solution techniques for disjunctive temporal reasoning problems. - Ioannis Tsamardinos; Martha E. Pollack (Artif. Intell., 2003)

CTP: A New Constraint-Based Formalism for Conditional, Temporal Planning. - Ioannis Tsamardinos; Thierry Vidal; Martha E. Pollack (Constraints An Int. J., 2003)

Autominder: an intelligent cognitive orthotic system for people with memory impairment. - Martha E. Pollack; Laura E. Brown; Dirk Colbry; Colleen E. McCarthy; Cheryl Orosz; Bart Peintner; Sailesh Ramakrishnan; Ioannis Tsamardinos (Robotics Auton. Syst., 2003)

A theoretical characterization of linear SVM-based feature selection. - Douglas P. Hardin; Ioannis Tsamardinos; Constantin F. Aliferis (ICML, 2004)

A Novel Algorithm for Scalable and Accurate Bayesian Network Learning. - Laura E. Brown; Ioannis Tsamardinos; Constantin F. Aliferis (MedInfo, 2004)

Methods for Multi-Category Cancer Diagnosis from Gene Expression Data: A Comprehensive Evaluation to Inform Decision Support System Development. - Alexander R. Statnikov; Constantin F. Aliferis; Ioannis Tsamardinos (MedInfo, 2004)

A Comparison of Novel and State-of-the-Art Polynomial Bayesian Network Learning Algorithms. - Laura E. Brown; Ioannis Tsamardinos; Constantin F. Aliferis (AAAI, 2005)

Using the GEMS System for Cancer Diagnosis and Biomarker Discovery from Microarray Gene Expression Data. - Alexander R. Statnikov; Ioannis Tsamardinos; Constantin F. Aliferis (AAAI, 2005)

A Comparison of Bayesian Network Learning Algorithms from Continuous Data. - Lawrence D. Fu; Ioannis Tsamardinos (AMIA, 2005)

Using the GEMS System for Supervised Analysis of Cancer Microarray Gene Expression Data. - Alexander R. Statnikov; Ioannis Tsamardinos; Constantin F. Aliferis (AMIA, 2005)

A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis. - Alexander R. Statnikov; Constantin F. Aliferis; Ioannis Tsamardinos; Douglas P. Hardin; Shawn Levy (Bioinform., 2005)

GEMS: A system for automated cancer diagnosis and biomarker discovery from microarray gene expression data. - Alexander R. Statnikov; Ioannis Tsamardinos; Yerbolat Dosbayev; Constantin F. Aliferis (Int. J. Medical Informatics, 2005)

Research Paper: Text Categorization Models for High-Quality Article Retrieval in Internal Medicine. - Yindalon Aphinyanaphongs; Ioannis Tsamardinos; Alexander R. Statnikov; Douglas P. Hardin; Constantin F. Aliferis (J. Am. Medical Informatics Assoc., 2005)

Generating Realistic Large Bayesian Networks by Tiling. - Ioannis Tsamardinos; Alexander R. Statnikov; Laura E. Brown; Constantin F. Aliferis (FLAIRS, 2006)

The max-min hill-climbing Bayesian network structure learning algorithm. - Ioannis Tsamardinos; Laura E. Brown; Constantin F. Aliferis (Mach. Learn., 2006)

Causal Data Mining in Bioinformatics. - Ioannis Tsamardinos (ERCIM News, 2007)

Bounding the False Discovery Rate in Local Bayesian Network Learning. - Ioannis Tsamardinos; Laura E. Brown (AAAI, 2008)

A Strategy for Making Predictions Under Manipulation. - Laura E. Brown; Ioannis Tsamardinos (WCCI Causation and Prediction Challenge, 2008)

Multi-Source Causal Analysis: Learning Bayesian Networks from Multiple Datasets. - Ioannis Tsamardinos; Asimakis P. Mariglis (AIAI, 2009)

Permutation Testing Improves Bayesian Network Learning. - Ioannis Tsamardinos; Giorgos Borboudakis (ECML/PKDD (3), 2010)

Structure-based variable selection for survival data. - Vincenzo Lagani; Ioannis Tsamardinos (Bioinform., 2010)

Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation. - Constantin F. Aliferis; Alexander R. Statnikov; Ioannis Tsamardinos; Subramani Mani; Xenofon D. Koutsoukos (J. Mach. Learn. Res., 2010)

Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part II: Analysis and Extensions. - Constantin F. Aliferis; Alexander R. Statnikov; Ioannis Tsamardinos; Subramani Mani; Xenofon D. Koutsoukos (J. Mach. Learn. Res., 2010)

Learning Causal Structure from Overlapping Variable Sets. - Sofia Triantafilou; Ioannis Tsamardinos; Ioannis G. Tollis (AISTATS, 2010)

Using Constraint Optimization for Conflict Resolution and Detail Control in Activity Recognition. - Chrysi Filippaki; Grigoris Antoniou; Ioannis Tsamardinos (AmI, 2011)

A unified approach to estimation and control of the False Discovery Rate in Bayesian network skeleton identification. - Angelos P. Armen; Ioannis Tsamardinos (ESANN, 2011)

A constraint-based approach to incorporate prior knowledge in causal models. - Giorgos Borboudakis; Sofia Triantafilou; Vincenzo Lagani; Ioannis Tsamardinos (ESANN, 2011)

Information-Preserving Techniques Improve Chemosensitivity Prediction of Tumours Based on Expression Profiles. - Eleni G. Christodoulou; O. D. Røe; Amos Folarin; Ioannis Tsamardinos (EANN, 2011)

Risk Assessment Models for Diabetes Complications: A Survey of Available Online Tools. - Lefteris Koumakis; Franco Chiarugi; Vincenzo Lagani; Angelina Kouroubali; Ioannis Tsamardinos (MobiHealth, 2011)

SVM-based miRNA: MiRNA∗ duplex prediction. - Nestoras Karathanasis; Ioannis Tsamardinos; Angelos P. Armen; Panayiota Poirazi (BIBE, 2012)

Incorporating Causal Prior Knowledge as Path-Constraints in Bayesian Networks and Maximal Ancestral Graphs. - Giorgos Borboudakis; Ioannis Tsamardinos (ICML, 2012)

A Methodological Framework for Statistical Analysis of Social Text Streams. - Sophia Kleisarchaki; Dimitris Kotzinos; Ioannis Tsamardinos; Vassilis Christophides (ISIP, 2012)

Learning from Mixture of Experimental Data: A Constraint-Based Approach. - Vincenzo Lagani; Ioannis Tsamardinos; Sofia Triantafilou (SETN, 2012)

Scoring Bayesian Networks with Informative, Causal and Associative Priors - Giorgos Borboudakis; Ioannis Tsamardinos (CoRR, 2012)

To feature space and back: Identifying top-weighted features in polynomial Support Vector Machine models. - Laura E. Brown; Ioannis Tsamardinos; Douglas P. Hardin (Intell. Data Anal., 2012)

Towards Integrative Causal Analysis of Heterogeneous Data Sets and Studies. - Ioannis Tsamardinos; Sofia Triantafilou; Vincenzo Lagani (J. Mach. Learn. Res., 2012)

A bioinformatics approach for investigating the determinants of Drosha processing. - Nestoras Karathanasis; Ioannis Tsamardinos; Panayiota Poirazi (BIBE, 2013)

Scoring and Searching over Bayesian Networks with Causal and Associative Priors. - Giorgos Borboudakis; Ioannis Tsamardinos (UAI, 2013)

Learning Neighborhoods of High Confidence in Constraint-Based Causal Discovery. - Sofia Triantafilou; Ioannis Tsamardinos; Anna Roumpelaki (Probabilistic Graphical Models, 2014)

Don't use a cannon to kill the ... miRNA mosquito. - Nestoras Karathanasis; Ioannis Tsamardinos; Panayiota Poirazi (Bioinform., 2014)

Discovering and Exploiting Entailment Relationships in Multi-Label Learning. - Christina Papagiannopoulou; Grigorios Tsoumakas; Ioannis Tsamardinos (CoRR, 2014)

Discovering and Exploiting Deterministic Label Relationships in Multi-Label Learning. - Christina Papagiannopoulou; Grigorios Tsoumakas; Ioannis Tsamardinos (KDD, 2015)

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 (Pacific Symposium on Biocomputing, 2015)

Bayesian Network Learning with Discrete Case-Control Data. - Giorgos Borboudakis; Ioannis Tsamardinos (UAI, 2015)

Performance-Estimation Properties of Cross-Validation-Based Protocols with Simultaneous Hyper-Parameter Optimization. - Ioannis Tsamardinos; Amin Rakhshani; Vincenzo Lagani (Int. J. Artif. Intell. Tools, 2015)

Constraint-based causal discovery from multiple interventions over overlapping variable sets. - Sofia Triantafillou; Ioannis Tsamardinos (J. Mach. Learn. Res., 2015)

Towards Robust and Versatile Causal Discovery for Business Applications. - Giorgos Borboudakis; Ioannis Tsamardinos (KDD, 2016)

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 (PACBB, 2016)

Marginal Causal Consistency in Constraint-based Causal Learning. - Anna Roumpelaki; Giorgos Borboudakis; Sofia Triantafillou; Ioannis Tsamardinos (CFA@UAI, 2016)

Score-based vs Constraint-based Causal Learning in the Presence of Confounders. - Sofia Triantafillou; Ioannis Tsamardinos (CFA@UAI, 2016)

Erratum to: A comparative evaluation of data-merging and meta-analysis methods for reconstructing gene-gene interactions. - Vincenzo Lagani; Argyro D. Karozou; David Gomez-Cabrero; Gilad Silberberg; Ioannis Tsamardinos (BMC Bioinform., 2016)

A comparative evaluation of data-merging and meta-analysis methods for reconstructing gene-gene interactions. - Vincenzo Lagani; Argyro D. Karozou; David Gomez-Cabrero; Gilad Silberberg; Ioannis Tsamardinos (BMC Bioinform., 2016)

On User-Centric Modular QoE Prediction for VoIP Based on Machine-Learning Algorithms. - Paulos Charonyktakis; Maria Plakia; Ioannis Tsamardinos; Maria Papadopouli (IEEE Trans. Mob. Comput., 2016)

Massively-Parallel Feature Selection for Big Data. - Ioannis Tsamardinos; Giorgos Borboudakis; Pavlos Katsogridakis; Polyvios Pratikakis; Vassilis Christophides (CoRR, 2017)

Bootstrapping the Out-of-sample Predictions for Efficient and Accurate Cross-Validation. - Ioannis Tsamardinos; Elissavet Greasidou; Michalis Tsagris; Giorgos Borboudakis (CoRR, 2017)

SCENERY: a web application for (causal) network reconstruction from cytometry data. - Giorgos Papoutsoglou; Giorgos Athineou; Vincenzo Lagani; Iordanis Xanthopoulos; Angelika Schmidt; Szabolcs Elias; Jesper Tegnér; Ioannis Tsamardinos (Nucleic Acids Res., 2017)

Mining Free-Text Medical Notes for Suicide Risk Assessment. - Marios Adamou; Grigoris Antoniou; Elissavet Greasidou; Vincenzo Lagani; Paulos Charonyktakis; Ioannis Tsamardinos (SETN, 2018)

BioDataome: a collection of uniformly preprocessed and automatically annotated datasets for data-driven biology. - Kleanthi Lakiotaki; Nikolaos Vorniotakis; Michail Tsagris; Georgios Georgakopoulos; Ioannis Tsamardinos (Database J. Biol. Databases Curation, 2018)

Feature selection for high-dimensional temporal data. - Michail Tsagris; Vincenzo Lagani; Ioannis Tsamardinos (BMC Bioinform., 2018)

Feature selection with the R package MXM. - Michail Tsagris; Ioannis Tsamardinos (F1000Research, 2018)

On scoring Maximal Ancestral Graphs with the Max-Min Hill Climbing algorithm. - Konstantinos Tsirlis; Vincenzo Lagani; Sofia Triantafillou; Ioannis Tsamardinos (Int. J. Approx. Reason., 2018)

Constraint-based causal discovery with mixed data. - Michail Tsagris; Giorgos Borboudakis; Vincenzo Lagani; Ioannis Tsamardinos (Int. J. Data Sci. Anal., 2018)

Correction to: Constraint-based causal discovery with mixed data. - Michail Tsagris; Giorgos Borboudakis; Vincenzo Lagani; Ioannis Tsamardinos (Int. J. Data Sci. Anal., 2018)

Bootstrapping the out-of-sample predictions for efficient and accurate cross-validation. - Ioannis Tsamardinos; Elissavet Greasidou; Giorgos Borboudakis (Mach. Learn., 2018)

A unified approach for sparse dynamical system inference from temporal measurements. - Yannis Pantazis; Ioannis Tsamardinos (Bioinform., 2019)

Forward-Backward Selection with Early Dropping. - Giorgos Borboudakis; Ioannis Tsamardinos (J. Mach. Learn. Res., 2019)

A greedy feature selection algorithm for Big Data of high dimensionality. - Ioannis Tsamardinos; Giorgos Borboudakis; Pavlos Katsogridakis; Polyvios Pratikakis; Vassilis Christophides (Mach. Learn., 2019)

Latent Feature Representations for Human Gene Expression Data Improve Phenotypic Predictions. - Yannis Pantazis; Christos Tselas; Kleanthi Lakiotaki; Vincenzo Lagani; Ioannis Tsamardinos (BIBM, 2020)

Pathway Activity Score Learning for Dimensionality Reduction of Gene Expression Data. - Ioulia Karagiannaki; Yannis Pantazis; Ekaterini Chatzaki; Ioannis Tsamardinos (DS, 2020)

Putting the Human Back in the AutoML Loop. - Iordanis Xanthopoulos; Ioannis Tsamardinos; Vassilis Christophides; Eric Simon; Alejandro Salinger (EDBT/ICDT Workshops, 2020)

Tuning Causal Discovery Algorithms. - Konstantina Biza; Ioannis Tsamardinos; Sofia Triantafillou (PGM, 2020)

A generalised OMP algorithm for feature selection with application to gene expression data. - Michail Tsagris; Zacharias Papadovasilakis; Kleanthi Lakiotaki; Ioannis Tsamardinos (CoRR, 2020)

Inference of Stochastic Dynamical Systems from Cross-Sectional Population Data. - Anastasios Tsourtis; Yannis Pantazis; Ioannis Tsamardinos (CoRR, 2020)

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 (BIBE, 2021)

PROTEUS: Predictive Explanation of Anomalies. - Nikolaos Myrtakis; Ioannis Tsamardinos; Vassilis Christophides (ICDE, 2021)

On Predictive Explanation of Data Anomalies. - Nikolaos Myrtakis; Ioannis Tsamardinos; Vassilis Christophides (CoRR, 2021)

Extending greedy feature selection algorithms to multiple solutions. - Giorgos Borboudakis; Ioannis Tsamardinos (Data Min. Knowl. Discov., 2021)

Credit Card Fraud Detection with Automated Machine Learning Systems. - Vasilios Plakandaras; Periklis Gogas; Theophilos Papadimitriou; Ioannis Tsamardinos (Appl. Artif. Intell., 2022)

Don't lose samples to estimation. - Ioannis Tsamardinos (Patterns, 2022)

The $ gamma$γ-OMP Algorithm for Feature Selection With Application to Gene Expression Data. - Michail Tsagris; Zacharias Papadovasilakis; Kleanthi Lakiotaki; Ioannis Tsamardinos (IEEE ACM Trans. Comput. Biol. Bioinform., 2022)

A Meta-Level Learning Algorithm for Sequential Hyper-Parameter Space Reduction in AutoML. - Giorgos Borboudakis; Paulos Charonyktakis; Konstantinos Paraschakis; Ioannis Tsamardinos (CoRR, 2023)

A meta-level analysis of online anomaly detectors. - Antonios Ntroumpogiannis; Michail Giannoulis; Nikolaos Myrtakis; Vassilis Christophides; Eric Simon; Ioannis Tsamardinos (VLDB J., 2023)

Automated machine learning for genome wide association studies. - Kleanthi Lakiotaki; Zacharias Papadovasilakis; Vincenzo Lagani; Stefanos Fafalios; Paulos Charonyktakis; Michail Tsagris; Ioannis Tsamardinos (Bioinform., 2023)

Learning biologically-interpretable latent representations for gene expression data. - Ioulia Karagiannaki; Krystallia Gourlia; Vincenzo Lagani; Yannis Pantazis; Ioannis Tsamardinos (Mach. Learn., 2023)

Confidence Interval Estimation of Predictive Performance in the Context of AutoML. - Konstantinos Paraschakis; Andrea Castellani; Giorgos Borboudakis; Ioannis Tsamardinos (AutoML, 2024)

AutoML for Explainable Anomaly Detection (XAD). - Nikolaos Myrtakis; Ioannis Tsamardinos; Vassilis Christophides (Tannen's Festschrift, 2024)

ETIA: Towards an Automated Causal Discovery Pipeline. - Konstantina Biza; Antonios Ntroumpogiannis; Sofia Triantafillou; Ioannis Tsamardinos (DS (2), 2024)

AI-Based Suicide Risk Assessment. - Pavlos Charonyktakis; Grigoris Antoniou; Marios Adamou; Michael Doyle; Sotiris Batsakis; Ioannis Tsamardinos (IISA, 2024)

ChronoEpilogi: Scalable Time Series Selection with Multiple Solutions. - Etienne Vareille; Michele Linardi; Ioannis Tsamardinos; Vassilis Christophides (NeurIPS, 2024)

CLBO: Conditional Local Bayesian Optimization for Automated Machine Learning. - George Paterakis; Giorgos Borboudakis; Konstantinos Paraschakis; Paulos Charonyktakis; Ioannis Tsamardinos (SETN, 2024)

Towards Automated Causal Discovery: a case study on 5G telecommunication data. - Konstantina Biza; Antonios Ntroumpogiannis; Sofia Triantafillou; Ioannis Tsamardinos (CoRR, 2024)

Out-of-Sample Tuning for Causal Discovery. - Konstantina Biza; Ioannis Tsamardinos; Sofia Triantafillou (IEEE Trans. Neural Networks Learn. Syst., 2024)

Do We Really Need Imputation in AutoML Predictive Modeling? - George Paterakis; Stefanos Fafalios; Paulos Charonyktakis; Vassilis Christophides; Ioannis Tsamardinos (ACM Trans. Knowl. Discov. Data, 2024)

Data Glitches Discovery using Influence-based Model Explanations. - Nikolaos Myrtakis; Ioannis Tsamardinos; Vassilis Christophides (KDD (1), 2025)

Temporal Causal-based Simulation for Realistic Time-series Generation. - Nikolaos Gkorgkolis; Nikolaos Kougioulis; MingXue Wang; Bora Caglayan; Andrea Tonon; Dario Simionato; Ioannis Tsamardinos (CoRR, 2025)

A Comparative Analysis of Influence Signals for Data Debugging. - Nikolaos Myrtakis; Ioannis Tsamardinos; Vassilis Christophides (CoRR, 2025)