Publications
New 2024
Antonenko, E., Mechenich, M., Beigaitė, R., Žliobaitė, I. and Read, J. (2024). Backward inference in probabilistic Regressor Chains with distributional constraints. Symposium on Intelligent Data Analysis, accepted.
New 2023
Galbrun, E., Liu, L., Tang, H., Kaakinen, A., Zhang, Z., Zhang, Z. and Žliobaitė, I. (2023). The emergence of modern zoogeographic regions in Asia examined through climate–dental trait association patterns. Nature Communications 14: 8194. DOI
Raulo, A., Rojas-Briceno, A., Kroger, B., Laaksonen, A., Lamuela Orta, C., Nurmio, S., Peltoniemi, M., Lahti., L. and Žliobaitė, I. (2023). What are patterns of rise and decline? Royal Society Open Science. DOI (open access).
Foister, T., Žliobaitė, I., Wilson, O., Fortelius, M. and Tallavaara, M. (2023). Homo heterogenus: Variability in Early Pleistocene Homo Environments. Evolutionary Anthropology. DOI.
Arranz, S., Casanovas-Vilar, I. Žliobaitė, I., Abella, J., Angelone, Ch., Azanza, B., Bernor, R., Cirilli, O., DeMiguel, D., Furio, M., Pandolfi, L., Roblez, J., Sanchez, I., van den Hoek Ostende, L. and Alba, D. (2023). Paleoenvironmental inferences on the Late Miocene hominoid-bearing site of Can Llobateres (NE Iberian Peninsula): An ecometric approach based on functional dental traits. Journal of Human Evolution 185, 103441. DOI.
Mechenich, M. and Žliobaitė, I. (2023). Eco-ISEA3H, a machine learning ready spatial database for ecometric and species distribution modeling. Scientific Data. DOI (open access)
Lintulaakso, K., Tatti, N., Žliobaitė, I. (2023). Quantifying mammalian diets. Mammalian Biology 103, 53-67. DOI (open access).
New 2022
Beigaitė, R., Read, J., Žliobaitė, I. (2022). Multi-output Regression with Structurally Incomplete Target Labels: A Case Study of Modelling Global Vegetation Cover. Ecological Informatics 72, 101849. DOI (open access)
Beigaitė, R., Mechenich, M., Žliobaitė, I. (2022). Spatial cross-validation for globally distributed data. Discovery science, LNAI 13601, 127-140. DOI PDFpreprint
Parmezan, A., Souza, V., Seth, A., Žliobaitė, I., Batista, G. (2022). Hierarchical classification of pollinating flying insects under changing environments. Ecological informatics 70, 101751. DOI PDFpreprint
Žliobaitė, I. (2022). Recommender systems for fossil community distribution modelling. Methods in ecology and evolution 13(8): 1609-1706. DOI (open access)
Beigaitė, R., Tang, H., Bryn, A., Skarpaas, O., Stordal, F., Bjerke, J. W. and Žliobaitė, I. (2022). Identifying climate thresholds for dominant natural vegetation types at the global scale using machine learning: Average climate versus extremes. Global Change Biology 28(11), 3557-3579. DOI (open access)
Toivonen, J., Fortelius, M. and Žliobaitė, I. (2022). Do species factories exist? Detecting exceptional patterns of evolution in the mammalian fossil record. Proceedings B of the Royal Society 289, 20212294. DOI (open access)
Žliobaitė, I. and Fortelius, M. (2022). On calibrating the completometer for the mammalian fossil record. Paleobiology 48(1): 1-11. DOI (open access)
Journal publications
Saarinen, J., Oksanen, O., Žliobaitė, I., Fortelius, M., DeMiguel, D., Azanza, B., Bocherens, H., Luzón, C., Yravedra, J., Courtenay, L., Blain, H.A., Sánchez-Bandera, Ch., Serrano-Ramos, A., Rodriguez-Alba, J.J., Viranta, S., Barsky, D., Solano-García, J., Tallavaara, M., Oms, O., Agustí, J., Ochando, J., Carrión, J.S., Jiménez-Arenas, J.M. (2021). Pliocene to Middle Pleistocene climate history in the Guadix-Baza Basin, and the environmental conditions of early Homo dispersal in Europe. Quaternary Science Reviews 268, 107132. DOI (open access)
Galbrun, E., Tang, H., Kaakinen, A., Žliobaitė, I. (2021). Redescription mining for analyzing local limiting conditions: A case study on the biogeography of large mammals in China and southern Asia. Ecological Informatics 63, 101314. DOI (open access)
Parmezan, A., Souza, V., Žliobaitė, I. and Batista, G. (2021). Changes in the wing-beat frequency of bees and wasps depending on environmental conditions: a study with optical sensors. Apidologie 52: 731-748. DOI
Fortelius, M., Myrdal, P. and Žliobaitė, I. (2021). The best of all possible coexistence. Palaeobiodiversity and Palaeoenvironments 101: 259-265. DOI
Žliobaitė, I. and Fortelius, M. (2020). All sizes fit the Red Queen. Paleobiology 46 (4): 478-494. DOI (open access)
Rannikko, J., Adhikari, H., Karme, A., Žliobaitė, I., Fortelius, M. (2020). The case of the grass‐eating suids in the Plio‐Pleistocene Turkana Basin: 3D dental topography in relation to diet in extant and fossil pigs. Journal of Morphology 281 (3): 348-364. DOI
Žliobaitė, I. (2019). Revisiting the biogeography of livestock animal domestication. Evolutionary Ecology Research 20, 657-678. DOI PDFpreprint
Oksanen, O., Žliobaitė, I., Saarinen, J., Lawing, A.M., Fortelius, M. (2019). A Humboldtian approach to life and climate of the geological past: Estimating palaeotemperature from dental traits of mammalian communities. Journal of Biogeography 46 (8), 1760--1776. DOI
Žliobaitė, I. (2019). Concept drift over geological times: predictive modeling baselines for analyzing the mammalian fossil record. Data mining and knowledge discovery 33(3), 773--803. preprint 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. Palaeontologia Electronica, Article number: 21.1.3A. link (open access)
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 blogpost 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. DOI PDFpreprint
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
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
Žliobaitė, I., Hollmén, J. (2015). Optimizing regression models for data streams with missing values. Machine Learning 99(1), p. 47-73. DOI PDF
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
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. DOI PDF 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, essays, opinion pieces
Žliobaitė, I. (2021). Did the Australopithecus anamensis-Australopithecus afarensis lineage wax and wane? A commentary to Du et al. (2020). Journal of Human Evolution 150, 102872. DOI
Žliobaitė, I. (2020). On the nature of time information in the fossil record. Medium text
Žliobaitė, I. (2020). Objectivity in evolutionary sciences: can data speak for themselves? Nature Ecology and Evolution blog text
Žliobaitė, I. (2019). There will be no single AI culture. Unpublished essay.
Žliobaitė, I. (2019). AI minds need to think about energy constraints. Nature Machine Intelligence 1, 335. FullText .
Fortelius, M., Bibi, F., Tang, H., Žliobaitė, I., Eronen, J., Kaya, F. (2019). The nature of the Old World savannah palaeobiome. Nature Ecology and Evolution 3, 504. DOI blogpost preprint
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. link (open access)
Žliobaitė, I. (2017). Fairness-aware machine learning: a perspective. arXiv:1708.00754 DOI
Žliobaitė, I. and Fortelius, M. (2016). Peer review: revise rules on conflict of interest. Nature 539(7628). 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. DOI PDFpreprint
Žliobaitė, I., Tatti, N. (2016). A note on adjusting R2 for using with cross-validation. arXiv:1605.01703 DOI
Žliobaitė, I. (2013). How good is the Electricity benchmark for evaluating concept drift adaptation. arXiv: 1301.3524 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
Žliobaitė, I. (2021). Recommender systems meet species distribution modelling. Proc. of Workshop on Perspectives on the Evaluation of Recommender Systems at RecSys'21. PDF
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. DOI PDF 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. DOI PDF
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. DOI PDF
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
Beigaitė, R., Read, J., Žliobaitė, I. (2020). Multi-output prediction of global vegetation distribution with incomplete data. Proc. of ICML workshop on The Art of Learning with Missing Values (ARTEMISS). PDF
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
+ domestication
Žliobaitė, I. (2020). Tooth wear rates of mammalian herbivores revisited: what is the baseline? Technical report.
Žliobaitė, I., Fortelius, M. (2019). The scaling of expansive energy under the Red Queen predicts Cope's Rule. BiorXiv .
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. 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 Thesis visualized by Wordle