Ioannis Tsamardinos

Laura E. Brown, Ioannis Tsamardinos, Douglas P. Hardin: To feature space and back: Identifying top-weighted features in polynomial Support Vector Machine models. Intell. Data Anal. 16(4): 551-579 (2012)

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

Vincenzo Lagani, Ioannis Tsamardinos, Sofia Triantafilou: Learning from Mixture of Experimental Data: A Constraint-Based Approach. SETN 2012: 124-131

Giorgos Borboudakis, Ioannis Tsamardinos: Scoring Bayesian Networks with Informative, Causal and Associative Priors. CoRR abs/1209.6561 (2012)

Chrysi Filippaki, Grigoris Antoniou, Ioannis Tsamardinos: Using Constraint Optimization for Conflict Resolution and Detail Control in Activity Recognition. AmI 2011: 51-60

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

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

Lefteris Koumakis, Franco Chiarugi, Vincenzo Lagani, Angelina Kouroubali, Ioannis Tsamardinos: Risk Assessment Models for Diabetes Complications: A Survey of Available Online Tools. MobiHealth 2011: 46-53

Vincenzo Lagani, Ioannis Tsamardinos: Structure-based variable selection for survival data. Bioinformatics 26(15): 1887-1894 (2010)

Sofia Triantafilou, Ioannis Tsamardinos, Ioannis G. Tollis: Learning Causal Structure from Overlapping Variable Sets. Journal of Machine Learning Research - Proceedings Track 9: 860-867 (2010)

Constantin F. Aliferis, Alexander R. Statnikov, Ioannis Tsamardinos, Subramani Mani, Xenofon D. Koutsoukos: Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation. Journal of Machine Learning Research 11: 171-234 (2010)

Constantin F. Aliferis, Alexander R. Statnikov, Ioannis Tsamardinos, Subramani Mani, Xenofon D. Koutsoukos: Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part II: Analysis and Extensions. Journal of Machine Learning Research 11: 235-284 (2010)

Ioannis Tsamardinos, Giorgos Borboudakis: Permutation Testing Improves Bayesian Network Learning. ECML/PKDD (3) 2010: 322-337

Ioannis Tsamardinos, Asimakis P. Mariglis: Multi-Source Causal Analysis: Learning Bayesian Networks from Multiple Datasets. AIAI 2009: 479-490

Laura E. Brown, Ioannis Tsamardinos: A Strategy for Making Predictions Under Manipulation. Journal of Machine Learning Research - Proceedings Track 3: 35-52 (2008)

Ioannis Tsamardinos, Laura E. Brown: Bounding the False Discovery Rate in Local Bayesian Network Learning. AAAI 2008: 1100-1105

Ioannis Tsamardinos: Causal Data Mining in Bioinformatics. ERCIM News 2007(69) (2007)

Ioannis Tsamardinos, Laura E. Brown, Constantin F. Aliferis: The max-min hill-climbing Bayesian network structure learning algorithm. Machine Learning 65(1): 31-78 (2006)

Ioannis Tsamardinos, Alexander R. Statnikov, Laura E. Brown, Constantin F. Aliferis: Generating Realistic Large Bayesian Networks by Tiling. FLAIRS Conference 2006: 592-597

Alexander R. Statnikov, Constantin F. Aliferis, Ioannis Tsamardinos, Douglas P. Hardin, Shawn Levy: A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis. Bioinformatics 21(5): 631-643 (2005)

Alexander R. Statnikov, Ioannis Tsamardinos, Yerbolat Dosbayev, Constantin F. Aliferis: GEMS: A system for automated cancer diagnosis and biomarker discovery from microarray gene expression data. I. J. Medical Informatics 74(7-8): 491-503 (2005)

Yindalon Aphinyanaphongs, Ioannis Tsamardinos, Alexander R. Statnikov, Douglas P. Hardin, Constantin F. Aliferis: Research Paper: Text Categorization Models for High-Quality Article Retrieval in Internal Medicine. JAMIA 12(2): 207-216 (2005)

Laura E. Brown, Ioannis Tsamardinos, Constantin F. Aliferis: A Comparison of Novel and State-of-the-Art Polynomial Bayesian Network Learning Algorithms. AAAI 2005: 739-745

Alexander R. Statnikov, Ioannis Tsamardinos, Constantin F. Aliferis: Using the GEMS System for Cancer Diagnosis and Biomarker Discovery from Microarray Gene Expression Data. AAAI 2005: 1710-1711

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

Ioannis Tsamardinos, Martha E. Pollack: Efficient solution techniques for disjunctive temporal reasoning problems. Artif. Intell. 151(1-2): 43-89 (2003)

Ioannis Tsamardinos, Thierry Vidal, Martha E. Pollack: CTP: A New Constraint-Based Formalism for Conditional, Temporal Planning. Constraints 8(4): 365-388 (2003)

Martha E. Pollack, Laura E. Brown, Dirk Colbry, Colleen E. McCarthy, Cheryl Orosz, Bart Peintner, Sailesh Ramakrishnan, Ioannis Tsamardinos: Autominder: an intelligent cognitive orthotic system for people with memory impairment. Robotics and Autonomous Systems 44(3-4): 273-282 (2003)

Constantin F. Aliferis, Ioannis Tsamardinos, P. Massion, Alexander R. Statnikov, Nafeh Fananapazir, Douglas P. Hardin: Machine Learning Models for Classification of Lung Cancer and Selection of Genomic Markers Using Array Gene Expression Data. FLAIRS Conference 2003: 67-71

Ioannis Tsamardinos, Constantin F. Aliferis, Alexander R. Statnikov: Algorithms for Large Scale Markov Blanket Discovery. FLAIRS Conference 2003: 376-381

Lewis Frey, Douglas H. Fisher, Ioannis Tsamardinos, Constantin F. Aliferis, Alexander R. Statnikov: Identifying Markov Blankets with Decision Tree Induction. ICDM 2003: 59-66

Ioannis Tsamardinos, Constantin F. Aliferis, Alexander R. Statnikov: Time and sample efficient discovery of Markov blankets and direct causal relations. KDD 2003: 673-678

Constantin F. Aliferis, Ioannis Tsamardinos, P. Massion, Alexander R. Statnikov, Douglas P. Hardin: Why Classification Models Using Array Gene Expression Data Perform So Well: A Preliminary Investigation of Explanatory Factors. METMBS 2003: 47-53

Constantin F. Aliferis, Ioannis Tsamardinos, Alexander R. Statnikov, Laura E. Brown: Causal Explorer: A Causal Probabilistic Network Learning Toolkit for Biomedical Discovery. METMBS 2003: 371-376

Alan Berfield, Panos K. Chrysanthis, Ioannis Tsamardinos, Martha E. Pollack, Sujata Banerjee: A Scheme for Integrating e-Services in Establishing Virtual Enterprises. RIDE 2002: 134-142

Ioannis Tsamardinos: A Probabilistic Approach to Robust Execution of Temporal Plans with Uncertainty. SETN 2002: 97-108

Martha E. Pollack, Colleen E. McCarthy, Sailesh Ramakrishnan, Ioannis Tsamardinos: Execution-Time Plan Management for a Cognitive Orthotic System. Advances in Plan-Based Control of Robotic Agents 2001: 179-192

Ioannis Tsamardinos, Martha E. Pollack, John F. Horty: Merging Plans with Quantitative Temporal Constraints, Temporally Extended Actions, and Conditional Branches. AIPS 2000: 264-272

Cristina Bicchieri, Martha E. Pollack, Carlo Rovelli, Ioannis Tsamardinos: The potential for the evolution of co-operation among web agents. Int. J. Hum.-Comput. Stud. 48(1): 9-29 (1998)

Ioannis Tsamardinos, Nicola Muscettola, Paul H. Morris: Fast Transformation of Temporal Plans for Efficient Execution. AAAI/IAAI 1998: 254-261

Nicola Muscettola, Paul H. Morris, Ioannis Tsamardinos: Reformulating Temporal Plans for Efficient Execution. KR 1998: 444-452