Publications

My research track record is roughly within four themes:

Next is a complete list of my publications in the chronological-venue order with links to preprints.

Journal publications

NEW/ In-press

  • Žliobaitė, I. (2018). Concept drift over geological times: predictive modeling baselines for analyzing the mammalian fossil record. Data mining and knowledge discovery, in press. PDFpreprint

2017-now

  • DeSantis, L., Fortelius, M., Grine, F. E., Janis, Ch., Kaiser, Th. M., Merceron, G., Purnell, M. A., Schultz-Kornas, E., Saarinen, J., Teaford, M., Ungar, P. S., Žliobaitė, I. (2018). The phylogenetic signal in tooth wear: What does it mean? Ecology and evolution 8 11359–11362. DOI (open access)
  • Žliobaitė, I., Tang, H., Saarinen, J., Fortelius, M., Rinne, J., Rannikko, J. (2018). Dental ecometrics of tropical Africa: linking vegetation types and communities of large plant-eating mammals. Evolutionary Ecology Research 19, p. 127-147. DOI.
  • Galbrun, E., Tang, H, Fortelius, M., Žliobaitė, I. (2018). Computational biomes: The ecometrics of large mammal teeth. Paleontologia Electronica, Article number: 21.1.3A DOI.
  • Žliobaitė, I. and Fortelius, M. (2018). Dental functional morphology predicts the scaling of chewing rate in mammals. Journal of Biomechanics 63(23), p. 32-36. DOI PDFpreprint
  • Kaya, F., Bibi, F., Žliobaitė, I., Eronen, J., Hui, T., Fortelius, M. (2018). The rise and fall of the Old World savannah fauna and the origins of the African savannah biome. Nature Ecology & Evolution 2 241-246. DOI
  • Žliobaitė, I., Fortelius, M., Stenseth, N. Chr. (2017). Reconciling taxon senescence with the Red Queen’s hypothesis. Nature 552, p. 92-95. DOI commentary press
  • Rannikko, J., Žliobaitė, I. and Fortelius, M. (2017). Relative abundances and palaeoecology of four suid genera in the Turkana Basin, Kenya, during late Miocene to Pleistocene. Palaeogeography, Palaeoclimatology, Palaeoecology 487, 187-193. DOI
  • Stegmann, R. A., Žliobaitė, I., Tolvanen, T., Hollmen, J., Read, J. (2017). A survey of evaluation methods for personal route and destination prediction from mobility traces. WIREs Data Mining and Knowledge Discovery 8(2), e1237. DOI
  • Žliobaitė, I., Puolamaki, K., Eronen, J. and Fortelius, M. (2017). A survey of computational methods for fossil data analysis. Evolutionary Ecology Research 18, 477-502. PDFpreprint DOI (open access)
  • Žliobaitė, I. (2017). Measuring discrimination in algorithmic decision making. Data Mining and Knowledge Discovery 31(4), 1060-1089. PDF DOI
  • Patterson, D.B., Braun, D.R., Behrensmeyer, A.K., Merritt, S., Žliobaitė, I., Reeves, J.S., Wood, B.A., Fortelius, M., Bobe, R. (2017). Ecosystem evolution and hominin paleobiology at East Turkana, northern Kenya between 2.0 and 1.4 Ma. Palaeogeography, Palaeoclimatology, Palaeoecology, 481(1), p. 1–13. link (open access)
  • Teittinen, J., Hiienkari, M., Žliobaitė, I. Hollmen, J., Berg, H., Heiskala, J., Viitanen, T., Simonsson, J., Koskinen, L. (2017). A 5.3 pJ/op approximate TTA VLIW tailored for machine learning. Microelectronics Journal 61, p. 106-113. DOI

2015-2016

  • Kulmala L., Žliobaitė I., Nikinmaa E., Nöjd P., Kolari P., Kabiri Koupaei K., Hollmén J., Mäkinen H. (2016). Environmental control of growth variation in a boreal Scots pine stand – a data-driven approach. Silva Fennica 50(5), article 1680. link (open access)
  • Žliobaitė, I., Rinne, J., Toth, A., Mechenich, M., Liu, L., Behrensmeyer, A.K., Fortelius, M. (2016). Herbivore teeth predict climatic limits in Kenyan ecosystems. PNAS 113(45), p. 12751-12756, link (open access)
  • Žliobaitė, I. and Custers, B. (2016). Using sensitive personal data may be necessary for avoiding discrimination in data-driven decision models. Artificial Intelligence and Law, 24(2), p. 183-201. DOI PDF [this PDF has a corrected version of formulae in the appendix]
  • Fortelius, M., Žliobaitė, I., Kaya, F., Bibi, F., Bobe, R., Leakey, L., Leakey, M., Patterson, D., Rannikko, J., Werdelin, L. (2016). An ecometric analysis of the fossil mammal record of the Turkana Basin. Philosophical Transactions B 371(1698), p. 1-13. link (open access)
  • Žliobaitė, I. and Khokhlov, M. (2016). Optimal estimates for short horizon travel time prediction in urban areas. Intelligent Data Analysis 20(6), p. 1459–1475. DOI PDF
  • Read, J., Žliobaitė, I. and Hollmén, J. (2016). Labeling Sensing Data for Mobility Modeling. Information Systems 57, p. 207-222. DOI PDF
  • Read, J., Žliobaitė, I. and Hollmén, J. (2016). Labeling Sensing Data for Mobility Modeling. Information Systems 57, p. 207-222. DOI PDF
  • Žliobaitė, I., Hollmén, J. (2015). Optimizing regression models for data streams with missing values. Machine Learning 99(1), p. 47-73. PDF DOI
  • Žliobaitė, I., Budka, M., Stahl, F. (2015). Towards cost-sensitive adaptation: when is it worth updating your predictive model Neurocomputing 150(A), p. 240-249. DOI PDF
  • Žliobaitė, I., Bifet, A., Read, J., Pfahringer, B., Holmes, G. (2015). Evaluation methods and decision theory for classification of streaming data with temporal dependence. Machine Learning 98(3), p. 455-482. DOI PDF

2009-2014

  • Žliobaitė, I., Hollmén, J., Koskinen, L., and Teittinen, J. (2014). Towards hardware-driven design of low-energy algorithms for data analysis. SIGMOD Record 43(4), p. 15-20. PDF DOI project_poster
  • Krempl, G., Žliobaitė, I., Brzezinski, D., Hullermeier, E., Last, M., Lemaire, V., Noack, T., Shaker, A., Sievi, S., Spiliopoulou, M., Stefanowski, J. (2014). Open Challenges for Data Stream Mining Research. SIGKDD Explorations 16(1), p. 1-10. DOI PDF
  • Žliobaitė, I., Hollmén, J., H. Junninen. (2014). Regression models tolerant to massively missing data: a case study in solar radiation nowcasting. Atmospheric Measurement Techniques Discussions 7, 7137-7174. DOI PDF Atmospheric Measurement Techniques 7, 4387-4399. DOI
  • Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., Bouchachia, A. (2014). A Survey on Concept Drift Adaptation. ACM Computing Surveys 46(4), Article No. 44. DOI PDF
  • Bose, R.P.J.C., van der Aalst, W.M.P., Žliobaitė, I., Pechenizkiy, M. (2014). Dealing with Concept Drifts in Process Mining. IEEE Transactions on Neural Networks and Learning Systems 25(1), 154-171. DOI
  • Žliobaitė, I. (2014). Controlled Permutations for Testing Adaptive Learning Models. Knowledge and Information Systems, 39(3), 565-578. DOI PDF data
  • Žliobaitė, I., Bifet, A., Pfahringer, B., Holmes, G. (2014). Active Learning with Drifting Streaming Data. IEEE Transactions on Neural Networks and Learning Systems 25(1), p. 27-39. DOI PDFpreprint
  • Ang, H. H., Gopalkrishnan, V., Žliobaitė, I., Pechenizkiy, M., Hoi, S. C. H. (2013). Predictive Handling of Asynchronous Concept Drifts in Distributed Environments. IEEE Transactions on Knowledge and Data Engineering 25(10), p. 2343-2355. DOI PDF
  • Žliobaitė, I. and Gabrys, B. (2014). Adaptive Preprocessing for Streaming Data. IEEE Transactions on Knowledge and Data Engineering 26(2), p. 309-321. DOI PDF
  • Kamiran, F., Žliobaitė, I. and Calders, T. (2013). Quantifying explainable discrimination and removing illegal discrimination in automated decision making. Knowledge and Information Systems 35(3), p. 613-644. DOI PDF code
  • Žliobaitė, I., Bifet, A., Gaber, M., Gabrys, B., Gama, J., Minku, L. and Musial, K. (2012). Next challenges for adaptive learning systems. SIGKDD Explorations 14(1), p. 48-55. DOI PDF
  • Žliobaitė, I., Bakker, J. and Pechenizkiy, M. (2012). Beating the baseline prediction in food sales: How intelligent an intelligent predictor is? Expert Systems with Applications 39(1), p. 806-815. DOI PDF
  • Žliobaitė, I. (2011). Combining similarity in time and space for training set formation under concept drift. Intelligent Data Analysis 15(4), p. 589-611. DOI PDF data
  • Pechenizkiy, M., Bakker, J., Žliobaitė, I., Ivannikov, A., Karkkainen, T. (2009). Online Mass Flow Prediction in CFB Boilers with Explicit Detection of Sudden Concept Drift. SIGKDD Explorations 11(2), p. 109-116. DOI PDF
  • Kuncheva, L.I. and Žliobaitė, I. (2009). On the Window Size for Classification in Changing Environments. Intelligent Data Analysis 13(6), p. 861-872. DOI PDF

Commentaries

  • Žliobaitė, I. and Fortelius, M. (2016). Peer review: revise rules on conflict of interest. Nature 539(7628), DOI PDFpublished PDFlongversion
  • Žliobaitė, I. and Stenseth, N. Chr. (2016). Improving Adaptation through Evolution and Learning: A Response to Watson and Szathmáry. Trends in Ecology and Evolution 31(12), p. 892-893. PDFpreprint DOI

Book chapters

  • Žliobaitė, I., Pechenizkiy, M. and Gama, J. (2016). An overview of concept drift applications. Big Data Analysis: New Algorithms for a New Society. Japkowicz, N. and Stefanowski, J. (Eds.), Springer, p. 91-114. DOI PDF
  • Kamiran, F. and Žliobaitė, I. (2013). Explainable and Non-explainable Discrimination in Classification. Discrimination and Privacy in the Information Society, series: Studies in Applied Philosophy, Epistemology and Rational Ethics, Vol. 3. Custers, B.; Zarsky, T.; Schermer, B.; Calders, T. (Eds.), Springer, p. 155-170. DOI PDF
  • Calders, T. and Žliobaitė, I. (2013). Why Unbiased Computational Processes Can Lead to Discriminative Decision Procedures. Discrimination and Privacy in the Information Society, series: Studies in Applied Philosophy, Epistemology and Rational Ethics, Vol. 3. Custers, B.; Zarsky, T.; Schermer, B.; Calders, T. (Eds.), Springer, p. 43-57. DOI PDF
  • Žliobaitė, I. (2011). Three Data Partitioning Strategies for Building Local Classifiers. Ensembles in Machine Learning Applications, series: Studies in Computational Intelligence, Vol. 373. Valentini, G., Re, M., Okun, O. (Eds.), Springer, p. 233-250. DOI PS
  • Žliobaitė, I. (2007). Introduction of New Expert and Old Expert Retirement under Concept Drift. Progress in Pattern Recognition, series: Advances in Computer Vision and Pattern Recognition, XIII. S. Singh, M. Singh (Eds.), Springer, p. 64-74. PDF

Full papers in conference proceedings

  • Maslov, A., Pechenizkiy, M., Pei, Y., Žliobaitė, I. Shklyaev, A., Kärkkäinen, T., Hollmén, J. (2017). BLPA: Bayesian Learn-Predict-Adjust Method for Online Detection of Recurrent Changepoints. Proc. of the 30th Int. Joint Conf. on Neural Networks (IJCNN), p. 1916-1923. DOI PDF
  • Krilavičius, T., Žliobaitė, I., Simonavičius, H., Jaruševičius, L. (2016). Predicting respiratory motion for real-time tumour tracking in radiotherapy. Proc. of IEEE 29th Int. Symposium on Computer-Based Medical Systems (CBMS), p. 7-12. DOI PDF
  • Maslov, A., Pechenizkiy, M., Žliobaitė, I. and Kärkkäinen, T. (2016). Modelling Recurrent Events for Improving Online Change Detection. Proc. of SIAM Int. Conf. on Data Mining (SDM), p. 549-557. DOI PDF
  • Martin Salvador M., Gabrys, B., Žliobaitė, I. (2014). Online Detection of Shutdown Periods in Chemical Plants: A Case Study. Procedia Computer Science 35, p. 580-588. DOI
  • Žliobaitė, I., Hollmen, J., Koskinen, L., Teittinen, J. (2014). Towards hardware-driven design of low-energy algorithms for data analysis. SIGMOD Record 43(4), p. 15-20. PDF DOI project_poster
  • Budka, M., Eastwood, M., Gabrys, B., Kadlec, P., Martin-Salvador, M., Schwan, S., Tsakonas, A., and Žliobaitė, I. (2014). From Sensor Readings to Predictions: on the Process of Developing Practical Soft Sensors. Proc. of The 13th Int. Symposium on Intelligent Data Analysis, Springer LNCS 8819, p. 49-60. DOI PDF
  • Žliobaitė, I., Hollmén, J. (2013). Fault tolerant regression for sensor data. Proc. of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD'13), Springer LNAI 8188, p. 449-464. PDF DOI
  • Bifet, A., Read, J., Žliobaitė, I., Pfahringer, B., Holmes, G. (2013). Pitfalls in benchmarking data stream classification and how to avoid them. Proc. of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD'13), Springer LNAI 8188, p. 465-479. PDF DOI
  • Ienco, D., Bifet, A., Žliobaitė, I., Pfahringer, B. (2013). Clustering Based Active Learning for Evolving Data Streams. Proc. of the 16th Int. Conf. on Discovery Science (DS'13), Springer LNCS 8140, p. 79-93. PDF DOI code
  • Bifet, A., Read, J., Pfahringer, B., Holmes, G., Žliobaitė, I. (2013). CD-MOA: Change Detection Framework for Massive Online Analysis. Proc. of the 20th Int. Symposium on Intelligent Data Analysis (IDA'13), p. 92-103. DOI PDF
  • Apeh, E., Žliobaitė, I., Pechenizkiy, M., Gabrys, B. (2012). Predicting Multi-Class Customer Profiles Based on Transactions: a Case Study in Food Sales. Proc. of the 32nd Annual Int. Conf. of the British Computer Society's Specialist Group on Artificial Intelligence (SGAI'12), Research and Development in Intelligent Systems XXIX, p. 213-218. DOI PDF PDFlong
  • Žliobaitė, I., Kamiran, F., Calders, T. (2011). Handling Conditional Discrimination. Proc. of the 11th IEEE Int. Conf. on Data Mining (ICDM'11), p. 992 - 1001. DOI PDF code
  • Žliobaitė, I. (2011). Controlled Permutations for Testing Adaptive Classifiers. Proc. of the 14th International Conf. on Discovery Science (DS'11) , Springer LNCS 6926, p. 365-379. DOI PDF code
  • Mazhelis, O., Žliobaitė, I., Pechenizkiy, M. (2011). Context-aware Personal Route Recognition. Proc. of the 14th International Conf. on Discovery Science (DS'11) , Springer LNCS 6926, p. 221-235. DOI PDF
  • Žliobaitė, I., Bifet, A., Pfahringer, B., Holmes, G. (2011). Active Learning with Evolving Streaming Data. Proc. of the 21st European Conf. on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD'11), Springer LNCS 6913, p. 597-612. DOI PDF code
  • Bose, R.P.J.C., van der Aalst, W.M.P., Žliobaitė, I., Pechenizkiy, M. (2011). Handling Concept Drift in Process Mining. Proc. of the 23rd Int. Conf. on Advanced Information Systems Engineering (CAiSE'11), Springer LNCS 6741, p. 391-405. DOI PDF
  • Žliobaitė, I. (2011). Identifying Hidden Contexts in Classification. Proc. of the 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'11), Springer LNAI 6634, p. 277-288. DOI PDF
  • Pechenizkiy, M., Vasilyeva, E., Žliobaitė, I., Tesanovic, A., Manev, G. (2010). Heart Failure Hospitalization Prediction in Remote Patient Management Systems. In: Dillon et al. (Eds), Proc. of the 23rd International Symposium on Computer-Based Medical Systems (CBMS '10), IEEE Press, p. 44-50. DOI PDF
  • Žliobaitė, I., Bakker, J., Pechenizkiy M. (2009). OMFP: An Approach for Online Mass Flow Prediction in CFB Boilers. Proc. of the 12th International Conf. on Discovery Science (DS 2009), Springer LNAI 5808, p. 272-286. DOI PDF
  • Žliobaite, I. (2009). Combining Time and Space Similarity for Small Size Learning under Concept Drift. Proc. of the 18th Int. Symposium on Methodologies for Intelligent Systems (ISMIS'09), Springer LNCS 5722, p. 412-421. DOI PDF
  • Žliobaitė, I. (2008). Expected Classification Error of the Euclidean Linear Classifier under Sudden Concept Drift. Proc. of the 5th int. conference on Fuzzy Systems and Knowledge Discovery (FSKD'08), IEEE Computer Society: vol 2, p. 29-33. DOI PDF
  • Žliobaitė, I. (2007). Ensemble Learning for Concept Drift Handling – the Role of New Expert. Poster Proceedings of the 5th int. conf. on Machine Learning and Data Mining in Pattern Recognition (MLDM'07), p. 251-260. PDF
  • Raudys, Š., Žliobaitė, I. (2006). The Multi-Agent System for Prediction of Financial Time Series. Proc. of the 8th int. conf. on Artificial Intelligence and Soft Computing (ICAISC'06), Springer LNAI 4029, p. 653-662. DOI
  • Raudys, Š., Žliobaitė, I. (2005). Prediction of Commodity Prices in Rapidly Changing Environments. Pattern Recognition and Data Mining, proc. of the 3rd int. conf. on Advances in Pattern Recognition (ICAPR'05), Springer LNCS 3686, p. 154-163. DOI

Full papers in workshop proceedings

  • Žliobaitė, I. (2015). On the relation between accuracy and fairness in binary classification. The 2nd workshop on Fairness, Accountability, and Transparency in Machine Learning (FATML) at ICML'15. arXiv
  • Žliobaitė, I., Mathioudakis, M., Lehtiniemi, T., Parviainen, P., Janhunen, T. (2015). Accessibility by public transport predicts residential real estate prices: a case study in Helsinki region. Proc. of the 2nd workshop on Mining Urban Data (MUD2), 65-71. PDFpreprint
  • Ienco, D., Pfahringer, B. and Žliobaitė, I. (2014). High density-focused uncertainty sampling for active learning over evolving stream data. Proc. of 3rd int. workshop on Big Data Mining, JMLR W&CP 36, p. 133–148. DOI PDF
  • Žliobaitė, I. and Hollmén, J. (2014). Mobile Sensing Data for Urban Mobility Analysis: A Case Study in Preprocessing. Proc. of Mining Urban Data workshop at EDBT/ICDT, p. 309-314. PDF data
  • Žliobaitė, I., Bifet, A., Holmes, G., Pfahringer, B. (2011). MOA Concept Drift Active Learning Strategies for Streaming Data. Proc. of the 2nd Workshop on Applications of Pattern Analysis, JMLR Workshop and Conference Proceedings (17), p. 48-55. DOI PDF
  • Žliobaitė, I. (2010). Change with Delayed Labeling: when is it detectable? Proc. of 2010 IEEE int. conf. on Data Mining Workshops, the 5th Int. workshop on Chance Discovery (IWCD10) at ICDM'10, IEEE Computer Society, p. 843-850. DOI PDFpreprint
  • Žliobaitė, I., Pechenizkiy, M. (2010). Learning with Actionable Attributes: Attention – Boundary Cases! Proc. of 2010 IEEE int. conf. on Data Mining Workshops, Int. workshop on Domain Driven Data Mining (DDDM'10) at ICDM'10, IEEE Computer Society, p. 1021-1028. DOI PDF
  • Žliobaitė, I. (2010). Three Data Partitioning Strategies for Building Local Classifiers: an experiment. Proc. of SUEMA workshop at ECML PKDD'10, p.151-160. PDF slides
  • Žliobaitė, I., Bakker, J., Pechenizkiy, M. (2009). Towards Context Aware Food Sales Prediction. Proc. of 2009 IEEE int. conf. on Data Mining Workshops, int. workshop on Domain Driven Data Mining (DDDM'09), IEEE Computer Society, p. 94-99. DOI PDF
  • Žliobaitė, I., Kuncheva, L. (2009). Determining the Training Window for Small Sample Size Classification with Concept Drift. Proc. of 2009 IEEE int. conf. on Data Mining Workshops, the 1st int. workshop on Transfer Mining (TM'09), IEEE Computer Society, p. 447-452. DOI PDF
  • Bakker, J., Pechenizkiy, M, Žliobaitė, I., Ivannikov, A. and Kärkkäinen, T. (2009). Handling Outliers and Concept Drift in Online Mass Flow Prediction in CFB Boilers. Proc. of the 3rd int. workshop on Knowledge Discovery from Sensor Data (SensorKDD’09), p. 13-22. [Best Paper award] DOI PDF

Published extended abstracts / abstracts

  • Krilavičius, T., Užupytė, R., Žliobaitė, I., Simonavičius, H. (2013). Correlation of external markers and functional targets for respiration compensation in radiotherapy. Medical Physics in the Baltic States 11, p. 42-45 slides
  • Pechenizkiy, M., Žliobaitė, I. (2010). Handling Concept Drift in Medical Applications: Importance, Challenges and Solutions. In: Dillon et al. (Eds) Proc. of the 23rd International Symposium on Computer-Based Medical Systems (CBMS '10), IEEE Press, p. 5. [abstract] DOI
  • Žliobaitė, I., Bakker, J., Pechenizkiy, M. (2009). Context Aware Sales Prediction. Proc. of the 21st Benelux conference on Artificial Intelligence (BNAIC'09), p. 449-450. [extended abstract] PDF
  • Kuncheva, L., Žliobaitė, I. (2008). Linear Discriminant Classifier (LDC) for Streaming Data with Concept Drift. SSPR/SPR'08, Springer LNCS, p: 4. [abstract, invited talk] DOI

Edited proceedings and editorials

  • Gavaldà, R., Žliobaitė, I. Gama, J. (2017). Proceedings of the First Workshop on Data Science for Social Good (SoGood'16) in conjunction with ECMLPKDD 2016 CEUR Workshop Proceedings 1831, CEUR-WS.org. link
  • Bielza, C., Gama, J., Jorge, A. and Žliobaitė, I. (2015). Guest editors introduction: special issue of the ECMLPKDD 2015 journal track. Data Min. Knowl. Discov. 29(5), 1113-1115.
  • Bielza, C., Gama, J., Jorge, A. and Žliobaitė, I. (2015). Guest Editors introduction: special issue of the ECMLPKDD 2015 journal track. Machine Learning 100(2-3), p.157-159.
  • Krempl, G., Žliobaitė, I., Wang, Y. and Forman, G. (editors). Real-World Challenges for Data Stream Mining, Proceedings of the 1st International Workshop on Real-World Challenges for Data Stream Mining (RealStream 2013) in conjunction with ECMLPKDD 2013. ISBN 978-3-940961-97. PDF
  • Calders, T. and Žliobaitė, I. (2012). Preface: International workshop on discrimination and privacy-aware data mining. Proceedings of the 12th IEEE International Conference on Data Mining Workshops (ICDMW). DOI
  • Pechenizkiy, M. and Žliobaitė, I. (2012). Introduction to the special issue on handling concept drift in adaptive information systems. Evolving systems 4(1), p. 1-2. DOI
  • Khan, L., Pechenizkiy, M., Žliobaitė, I. (2011). Preface to the Handling Concept Drift and Reoccurring Contexts in Adaptive Information Systems Workshop. Proceedings of the 11th IEEE International Conference on Data Mining Workshops. DOI
  • Pechenizkiy, M., Žliobaitė, I. (editors). Proceedings of the First International Workshop on Handling Concept Drift in Adaptive Information Systems: Importance, Challenges and Solutions (HaCDAIS 2010) in conjunction with ECML PKDD 2010. PDF

Technical reports and non-peer-reviewed

  • Žliobaitė, I. (2017). Fairness-aware machine learning: a perspective. arXiv:1708.00754 DOI
  • Žliobaitė, I., Tatti, N. (2016). A note on adjusting R2 for using with cross-validation. arXiv:1605.01703 DOI
  • Žliobaitė, I., Khokhlov, M. (2015). Optimal estimates for short horizon travel time prediction in urban areas. arXiv: 1507.08444 DOI
  • Krilavičius, T., Žliobaitė, I. Simonavičius, H., Jaruševičius, L. (2015). Predicting respiratory motion for real-time tumour tracking in radiotherapy. arXiv: 1508.00749 DOI
  • Bose, R.P.J.C, van der Aalst, W. M. P., Žliobaitė, I. and Pechenizkiy, M. (2013). Dealing With Concept Drifts in Process Mining: A Case Study in a Dutch Municipality. BPM Center Report BPM-13-13, BPMcenter.org PDF
  • Žliobaitė, I. (2013). How good is the Electricity benchmark for evaluating concept drift adaptation. arXiv: 1301.3524 DOI PDF
  • Žliobaitė, I. and Pechenizkiy, M. (2010). Reference Framework for Handling Concept Drift: An Application Perspective. Technical report, Eindhoven University of Technology PDF
  • Žliobaitė, I. and Kuncheva, L. (2010). Theoretical Window Size for Classification in the Presence of Sudden Concept Drift. Technical Report, CS-TR-001-2010, Bangor University, UK PDF
  • Žliobaitė, I. (2009). Learning under Concept Drift: an Overview. Vilnius University, Technical Report PDF arXiv: 1010.4784 DOI
  • Žliobaitė, I. and Krilavičius, T. (2009). CLAN: Clustering for Credit Risk Assessment. An entry to PAKDD 2009 Data Mining Competition. PDF
  • Žliobaitė, I. (2009). On Use of Historical Information under Sudden and Gradual Concept Drift. Vilnius University, Faculty of Mathematics and Informatics, Technical Report 2009-02. PDF

PhD thesis

  • Žliobaitė, I. (2010). Adaptive Training Set Formation. Vilnius University, Lithuania. PDF slides Thesis visualized by Wordle