Prediction of Commodity Prices in Rapidly Changing Environments. - Sarunas Raudys; Indre Zliobaite (ICAPR (1), 2005)

The Multi-Agent System for Prediction of Financial Time Series. - Sarunas Raudys; Indre Zliobaite (ICAISC, 2006)

Ensemble Learning for Concept Drift Handling - the Role of New Expert. - Indre Zliobaite (MLDM Posters, 2007)

Expected Classification Error of the Euclidean Linear Classifier under Sudden Concept Drift. - Indre Zliobaite (FSKD (2), 2008)

Linear Discriminant Classifier (LDC) for Streaming Data with Concept Drift. - Ludmila I. Kuncheva; Indre Zliobaite (SSPR/SPR, 2008)

OMFP: An Approach for Online Mass Flow Prediction in CFB Boilers. - Indre Zliobaite; Jorn Bakker; Mykola Pechenizkiy (Discovery Science, 2009)

Determining the Training Window for Small Sample Size Classification with Concept Drift. - Indre Zliobaite; Ludmila I. Kuncheva (ICDM Workshops, 2009)

Towards Context Aware Food Sales Prediction. - Indre Zliobaite; Jorn Bakker; Mykola Pechenizkiy (ICDM Workshops, 2009)

Combining Time and Space Similarity for Small Size Learning under Concept Drift. - Indre Zliobaite (ISMIS, 2009)

Handling outliers and concept drift in online mass flow prediction in CFB boilers. - Jorn Bakker; Mykola Pechenizkiy; Indre Zliobaite; Andriy Ivannikov; Tommi Kärkkäinen (KDD Workshop on Knowledge Discovery from Sensor Data, 2009)

On the window size for classification in changing environments. - Ludmila I. Kuncheva; Indre Zliobaite (Intell. Data Anal., 2009)

Online mass flow prediction in CFB boilers with explicit detection of sudden concept drift. - Mykola Pechenizkiy; Jorn Bakker; Indre Zliobaite; Andriy Ivannikov; Tommi Kärkkäinen (SIGKDD Explor., 2009)

Heart failure hospitalization prediction in remote patient management systems. - Mykola Pechenizkiy; Ekaterina Vasilyeva; Indre Zliobaite; Aleksandra Tesanovic; Goran Manev (CBMS, 2010)

Handling concept drift in medical applications: Importance, challenges and solutions. - Mykola Pechenizkiy; Indre Zliobaite (CBMS, 2010)

Change with Delayed Labeling: When is it Detectable? - Indre Zliobaite (ICDM Workshops, 2010)

Learning with Actionable Attributes: Attention – Boundary Cases! - Indre Zliobaite; Mykola Pechenizkiy (ICDM Workshops, 2010)

Learning under Concept Drift: an Overview - Indre Zliobaite (CoRR, 2010)

Handling Concept Drift in Process Mining. - R. P. Jagadeesh Chandra Bose; Wil M. P. van der Aalst; Indre Zliobaite; Mykola Pechenizkiy (CAiSE, 2011)

Context-Aware Personal Route Recognition. - Oleksiy Mazhelis; Indre Zliobaite; Mykola Pechenizkiy (Discovery Science, 2011)

Controlled Permutations for Testing Adaptive Classifiers. - Indre Zliobaite (Discovery Science, 2011)

Handling Conditional Discrimination. - Indre Zliobaite; Faisal Kamiran; Toon Calders (ICDM, 2011)

Identifying Hidden Contexts in Classification. - Indre Zliobaite (PAKDD (1), 2011)

Active Learning with Evolving Streaming Data. - Indre Zliobaite; Albert Bifet; Bernhard Pfahringer; Geoff Holmes 001 (ECML/PKDD (3), 2011)

Combining similarity in time and space for training set formation under concept drift. - Indre Zliobaite (Intell. Data Anal., 2011)

MOA Concept Drift Active Learning Strategies for Streaming Data. - Indre Zliobaite; Albert Bifet; Geoff Holmes 001; Bernhard Pfahringer (WAPA, 2011)

Three Data Partitioning Strategies for Building Local Classifiers. - Indre Zliobaite (Ensembles in Machine Learning Applications, 2011)

Predicting Multi-class Customer Profiles Based on Transactions: a Case Study in Food Sales. - Edward Tersoo Apeh; Indre Zliobaite; Mykola Pechenizkiy; Bogdan Gabrys (SGAI Conf., 2012)

Beating the baseline prediction in food sales: How intelligent an intelligent predictor is? - Indre Zliobaite; Jorn Bakker; Mykola Pechenizkiy (Expert Syst. Appl., 2012)

Next challenges for adaptive learning systems. - Indre Zliobaite; Albert Bifet; Mohamed Medhat Gaber; Bogdan Gabrys; João Gama 001; Leandro L. Minku; Katarzyna Musial (SIGKDD Explor., 2012)

Clustering Based Active Learning for Evolving Data Streams. - Dino Ienco; Albert Bifet; Indre Zliobaite; Bernhard Pfahringer (Discovery Science, 2013)

CD-MOA: Change Detection Framework for Massive Online Analysis. - Albert Bifet; Jesse Read; Bernhard Pfahringer; Geoff Holmes 001; Indre Zliobaite (IDA, 2013)

Pitfalls in Benchmarking Data Stream Classification and How to Avoid Them. - Albert Bifet; Jesse Read; Indre Zliobaite; Bernhard Pfahringer; Geoff Holmes 001 (ECML/PKDD (1), 2013)

Fault Tolerant Regression for Sensor Data. - Indre Zliobaite; Jaakko Hollmén (ECML/PKDD (1), 2013)

Predictive User Modeling with Actionable Attributes. - Indre Zliobaite; Mykola Pechenizkiy (CoRR, 2013)

How good is the Electricity benchmark for evaluating concept drift adaptation - Indre Zliobaite (CoRR, 2013)

Introduction to the special issue on handling concept drift in adaptive information systems. - Mykola Pechenizkiy; Indre Zliobaite (Evol. Syst., 2013)

Quantifying explainable discrimination and removing illegal discrimination in automated decision making. - Faisal Kamiran; Indre Zliobaite; Toon Calders (Knowl. Inf. Syst., 2013)

Predictive Handling of Asynchronous Concept Drifts in Distributed Environments. - Hock Hee Ang; Vivekanand Gopalkrishnan; Indre Zliobaite; Mykola Pechenizkiy; Steven C. H. Hoi (IEEE Trans. Knowl. Data Eng., 2013)

Why Unbiased Computational Processes Can Lead to Discriminative Decision Procedures. - Toon Calders; Indre Zliobaite (Discrimination and Privacy in the Information Society, 2013)

Explainable and Non-explainable Discrimination in Classification. - Faisal Kamiran; Indre Zliobaite (Discrimination and Privacy in the Information Society, 2013)

Mobile Sensing Data for Urban Mobility Analysis: A Case Study in Preprocessing. - Indre Zliobaite; Jaakko Hollmén (EDBT/ICDT Workshops, 2014)

From Sensor Readings to Predictions: On the Process of Developing Practical Soft Sensors. - Marcin Budka; Mark Eastwood; Bogdan Gabrys; Petr Kadlec 002; Manuel Martin Salvador; Stephanie Schwan; Athanasios Tsakonas; Indre Zliobaite (IDA, 2014)

High density-focused uncertainty sampling for active learning over evolving stream data. - Dino Ienco; Indre Zliobaite; Bernhard Pfahringer (BigMine, 2014)

Online Detection of Shutdown Periods in Chemical Plants: A Case Study. - Manuel Martin Salvador; Bogdan Gabrys; Indre Zliobaite (KES, 2014)

A survey on concept drift adaptation. - João Gama 001; Indre Zliobaite; Albert Bifet; Mykola Pechenizkiy; Abdelhamid Bouchachia (ACM Comput. Surv., 2014)

Controlled permutations for testing adaptive learning models. - Indre Zliobaite (Knowl. Inf. Syst., 2014)

Open challenges for data stream mining research. - Georg Krempl; Indre Zliobaite; Dariusz Brzezinski; Eyke Hüllermeier; Mark Last; Vincent Lemaire 001; Tino Noack; Ammar Shaker; Sonja Sievi; Myra Spiliopoulou; Jerzy Stefanowski (SIGKDD Explor., 2014)

Towards Hardware-driven Design of Low-energy Algorithms for Data Analysis. - Indre Zliobaite; Jaakko Hollmén; Lauri Koskinen; Jukka Teittinen (SIGMOD Rec., 2014)

Adaptive Preprocessing for Streaming Data. - Indre Zliobaite; Bogdan Gabrys (IEEE Trans. Knowl. Data Eng., 2014)

Dealing With Concept Drifts in Process Mining. - R. P. Jagadeesh Chandra Bose; Wil M. P. van der Aalst; Indre Zliobaite; Mykola Pechenizkiy (IEEE Trans. Neural Networks Learn. Syst., 2014)

Active Learning With Drifting Streaming Data. - Indre Zliobaite; Albert Bifet; Bernhard Pfahringer; Geoffrey Holmes 001 (IEEE Trans. Neural Networks Learn. Syst., 2014)

Accessibility by Public Transport Predicts Residential Real Estate Prices: A Case Study in Helsinki Region. - Indre Zliobaite; Michael Mathioudakis; Tuukka Lehtiniemi; Pekka Parviainen; Tomi Janhunen (MUD@ICML, 2015)

On the relation between accuracy and fairness in binary classification. - Indre Zliobaite (CoRR, 2015)

A survey on measuring indirect discrimination in machine learning. - Indre Zliobaite (CoRR, 2015)

Towards cost-sensitive adaptation: When is it worth updating your predictive model? - Indre Zliobaite; Marcin Budka; Frederic T. Stahl (Neurocomputing, 2015)

Guest Editors introduction: special issue of the ECMLPKDD 2015 journal track. - Concha Bielza; João Gama 001; Alípio Jorge; Indre Zliobaite (Mach. Learn., 2015)

Evaluation methods and decision theory for classification of streaming data with temporal dependence. - Indre Zliobaite; Albert Bifet; Jesse Read; Bernhard Pfahringer; Geoff Holmes 001 (Mach. Learn., 2015)

Optimizing regression models for data streams with missing values. - Indre Zliobaite; Jaakko Hollmén (Mach. Learn., 2015)

Predicting Respiratory Motion for Real-Time Tumour Tracking in Radiotherapy. - Tomas Krilavicius; Indre Zliobaite; Henrikas Simonavicius; Laimonas Jarusevicius (CBMS, 2016)

Modelling Recurrent Events for Improving Online Change Detection. - Alexandr V. Maslov; Mykola Pechenizkiy; Indre Zliobaite; Tommi Kärkkäinen (SDM, 2016)

Using sensitive personal data may be necessary for avoiding discrimination in data-driven decision models. - Indre Zliobaite; Bart Custers (Artif. Intell. Law, 2016)

A note on adjusting $R^2$ for using with cross-validation. - Indre Zliobaite; Nikolaj Tatti (CoRR, 2016)

Optimal estimates for short horizon travel time prediction in urban areas. - Indre Zliobaite; Mikhail Khokhlov (Intell. Data Anal., 2016)

Labeling sensing data for mobility modeling. - Jesse Read; Indre Zliobaite; Jaakko Hollmén (Inf. Syst., 2016)

BLPA: Bayesian learn-predict-adjust method for online detection of recurrent changepoints. - Alexandr V. Maslov; Mykola Pechenizkiy; Yulong Pei; Indre Zliobaite; Alexander Shklyaev; Tommi Kärkkäinen; Jaakko Hollmén (IJCNN, 2017)

Fairness-aware machine learning: a perspective. - Indre Zliobaite (CoRR, 2017)

Measuring discrimination in algorithmic decision making. - Indre Zliobaite (Data Min. Knowl. Discov., 2017)

A 5.3 pJ/op approximate TTA VLIW tailored for machine learning. - Jukka Teittinen; Markus Hiienkari; Indre Zliobaite; Jaakko Hollmén; Heikki Berg; Juha Heiskala; Timo Viitanen; Jesse Simonsson; Lauri Koskinen (Microelectron. J., 2017)

A survey of evaluation methods for personal route and destination prediction from mobility traces. - Roelant A. Stegmann; Indre Zliobaite; Tuukka Tolvanen; Jaakko Hollmén; Jesse Read (WIREs Data Mining Knowl. Discov., 2018)

Concept drift over geological times: predictive modeling baselines for analyzing the mammalian fossil record. - Indre Zliobaite (Data Min. Knowl. Discov., 2019)

AI minds need to think about energy constraints. - Indre Zliobaite (Nat. Mach. Intell., 2019)

Recommender Systems Meet Species Distribution Modelling. - Indre Zliobaite (Perspectives@RecSys, 2021)

Redescription mining for analyzing local limiting conditions: A case study on the biogeography of large mammals in China and southern Asia. - Esther Galbrun; Hui Tang; Anu Kaakinen; Indre Zliobaite (Ecol. Informatics, 2021)

Spatial Cross-Validation for Globally Distributed Data. - Rita Beigaite; Michael Mechenich; Indre Zliobaite (DS, 2022)

Learning from Data Streams: An Overview and Update. - Jesse Read; Indre Zliobaite (CoRR, 2022)

Multi-output regression with structurally incomplete target labels: A case study of modelling global vegetation cover. - Rita Beigaite; Jesse Read; Indre Zliobaite (Ecol. Informatics, 2022)

Hierarchical classification of pollinating flying insects under changing environments. - Antonio Rafael Sabino Parmezan; Vinícius M. A. de Souza; Arpita Seth; Indre Zliobaite; Gustavo E. A. P. A. Batista (Ecol. Informatics, 2022)

A Historical Context for Data Streams. - Indre Zliobaite; Jesse Read (CoRR, 2023)

Backward Inference in Probabilistic Regressor Chains with Distributional Constraints. - Ekaterina Antonenko; Michael Mechenich; Rita Beigaite; Indre Zliobaite; Jesse Read (IDA (2), 2024)

Modelling the longevity of complex living systems. - Indre Zliobaite (CoRR, 2024)

Predicting habitat suitability for Asian elephants in non-analog ecosystems with Bayesian models. - Ryoko Noda; Michael Mechenich; Juha Saarinen; Aki Vehtari; Indre Zliobaite (Ecol. Informatics, 2024)

Supervised Learning from Data Streams: An Overview and Update. - Jesse Read; Indre Zliobaite (ACM Comput. Surv., 2025)