Indre Zliobaite

95 papers A* 1A 2B 12C 1Misc 1Journal 46Unranked 20
YearRankTypeTitle / Venue / Authors
2026 J jnl
What Do Learned Models Measure?
CoRR
Indre Zliobaite
2025 J jnl
Supervised Learning from Data Streams: An Overview and Update.
ACM Comput. Surv.
Jesse Read, Indre Zliobaite
2024 — conf
Backward Inference in Probabilistic Regressor Chains with Distributional Constraints.
IDA (2)
Ekaterina Antonenko, Michael Mechenich, Rita Beigaite, Indre Zliobaite, Jesse Read
2024 — ed.
Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9-13, 2024, Proceedings, Part I
ECML/PKDD (1)
Albert Bifet, Jesse Davis, Tomas Krilavicius, Meelis Kull, Eirini Ntoutsi, Indre Zliobaite
2024 — ed.
Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9-13, 2024, Proceedings, Part II
ECML/PKDD (2)
Albert Bifet, Jesse Davis, Tomas Krilavicius, Meelis Kull, Eirini Ntoutsi, Indre Zliobaite
2024 — ed.
Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9-13, 2024, Proceedings, Part III
ECML/PKDD (3)
Albert Bifet, Jesse Davis, Tomas Krilavicius, Meelis Kull, Eirini Ntoutsi, Indre Zliobaite
2024 — ed.
Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9-13, 2024, Proceedings, Part IV
ECML/PKDD (4)
Albert Bifet, Jesse Davis, Tomas Krilavicius, Meelis Kull, Eirini Ntoutsi, Indre Zliobaite
2024 — ed.
Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9-13, 2024, Proceedings, Part V
ECML/PKDD (5)
Albert Bifet, Jesse Davis, Tomas Krilavicius, Meelis Kull, Eirini Ntoutsi, Indre Zliobaite
2024 — ed.
Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9-13, 2024, Proceedings, Part VI
ECML/PKDD (6)
Albert Bifet, Jesse Davis, Tomas Krilavicius, Meelis Kull, Eirini Ntoutsi, Indre Zliobaite
2024 — ed.
Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9-13, 2024, Proceedings, Part VII
ECML/PKDD (7)
Albert Bifet, Jesse Davis, Tomas Krilavicius, Meelis Kull, Eirini Ntoutsi, Indre Zliobaite
2024 — ed.
Machine Learning and Knowledge Discovery in Databases. Research Track and Demo Track - European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9-13, 2024, Proceedings, Part VIII
ECML/PKDD (8)
Albert Bifet, Povilas Daniusis, Jesse Davis, Tomas Krilavicius, Meelis Kull, Eirini Ntoutsi, Kai Puolamäki, Indre Zliobaite
2024 J jnl
Modelling the longevity of complex living systems.
CoRR
Indre Zliobaite
2024 J jnl
Predicting habitat suitability for Asian elephants in non-analog ecosystems with Bayesian models.
Ecol. Informatics
Ryoko Noda, Michael Mechenich, Juha Saarinen, Aki Vehtari, Indre Zliobaite
2023 J jnl
A Historical Context for Data Streams.
CoRR
Indre Zliobaite, Jesse Read
2022 J jnl
Hierarchical classification of pollinating flying insects under changing environments.
Ecol. Informatics
Antonio Rafael Sabino Parmezan, Vinícius M. A. de Souza, Arpita Seth, Indre Zliobaite, Gustavo E. A. P. A. Batista
2022 J jnl
Learning from Data Streams: An Overview and Update.
CoRR
Jesse Read, Indre Zliobaite
2022 J jnl
Multi-output regression with structurally incomplete target labels: A case study of modelling global vegetation cover.
Ecol. Informatics
Rita Beigaite, Jesse Read, Indre Zliobaite
2022 B conf
Spatial Cross-Validation for Globally Distributed Data.
DS
Rita Beigaite, Michael Mechenich, Indre Zliobaite
2021 — conf
Recommender Systems Meet Species Distribution Modelling.
Perspectives@RecSys
Indre Zliobaite
2021 J jnl
Redescription mining for analyzing local limiting conditions: A case study on the biogeography of large mammals in China and southern Asia.
Ecol. Informatics
Esther Galbrun, Hui Tang, Anu Kaakinen, Indre Zliobaite
2019 J jnl
AI minds need to think about energy constraints.
Nat. Mach. Intell.
Indre Zliobaite
2019 J jnl
Concept drift over geological times: predictive modeling baselines for analyzing the mammalian fossil record.
Data Min. Knowl. Discov.
Indre Zliobaite
2018 J jnl
A survey of evaluation methods for personal route and destination prediction from mobility traces.
WIREs Data Mining Knowl. Discov.
Roelant A. Stegmann, Indre Zliobaite, Tuukka Tolvanen, Jaakko Hollmén, Jesse Read
2017 J jnl
A 5.3 pJ/op approximate TTA VLIW tailored for machine learning.
Microelectron. J.
Jukka Teittinen, Markus Hiienkari, Indre Zliobaite, Jaakko Hollmén, Heikki Berg, Juha Heiskala, Timo Viitanen, Jesse Simonsson, Lauri Koskinen
2017 B conf
BLPA: Bayesian learn-predict-adjust method for online detection of recurrent changepoints.
IJCNN
Alexandr V. Maslov, Mykola Pechenizkiy, Yulong Pei, Indre Zliobaite, Alexander Shklyaev, Tommi Kärkkäinen, Jaakko Hollmén
2017 J jnl
Fairness-aware machine learning: a perspective.
CoRR
Indre Zliobaite
2017 J jnl
Measuring discrimination in algorithmic decision making.
Data Min. Knowl. Discov.
Indre Zliobaite
2017 — ed.
Proceedings of the First Workshop on Data Science for Social Good co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Dicovery in Databases, SoGood@ECML-PKDD 2016, Riva del Garda, Italy, September 19, 2016.
SoGood@ECML-PKDD
Ricard Gavaldà, Indre Zliobaite, João Gama
2016 J jnl
A note on adjusting $R^2$ for using with cross-validation.
CoRR
Indre Zliobaite, Nikolaj Tatti
2016 J jnl
Labeling sensing data for mobility modeling.
Inf. Syst.
Jesse Read, Indre Zliobaite, Jaakko Hollmén
2016 A conf
Modelling Recurrent Events for Improving Online Change Detection.
SDM
Alexandr V. Maslov, Mykola Pechenizkiy, Indre Zliobaite, Tommi Kärkkäinen
2016 J jnl
Optimal estimates for short horizon travel time prediction in urban areas.
Intell. Data Anal.
Indre Zliobaite, Mikhail Khokhlov
2016 B conf
Predicting Respiratory Motion for Real-Time Tumour Tracking in Radiotherapy.
CBMS
Tomas Krilavicius, Indre Zliobaite, Henrikas Simonavicius, Laimonas Jarusevicius
2016 J jnl
Using sensitive personal data may be necessary for avoiding discrimination in data-driven decision models.
Artif. Intell. Law
Indre Zliobaite, Bart Custers
2015 J jnl
A survey on measuring indirect discrimination in machine learning.
CoRR
Indre Zliobaite
2015 — conf
Accessibility by Public Transport Predicts Residential Real Estate Prices: A Case Study in Helsinki Region.
MUD@ICML
Indre Zliobaite, Michael Mathioudakis, Tuukka Lehtiniemi, Pekka Parviainen, Tomi Janhunen
2015 J jnl
Evaluation methods and decision theory for classification of streaming data with temporal dependence.
Mach. Learn.
Indre Zliobaite, Albert Bifet, Jesse Read, Bernhard Pfahringer, Geoff Holmes
2015 J jnl
Guest Editors introduction: special issue of the ECMLPKDD 2015 journal track.
Mach. Learn.
Concha Bielza, João Gama, Alípio Jorge, Indre Zliobaite
2015 J jnl
Guest editors introduction: special issue of the ECMLPKDD 2015 journal track.
Data Min. Knowl. Discov.
Concha Bielza, João Gama, Alípio Jorge, Indre Zliobaite
2015 J jnl
On the relation between accuracy and fairness in binary classification.
CoRR
Indre Zliobaite
2015 J jnl
Optimal estimates for short horizon travel time prediction in urban areas.
CoRR
Indre Zliobaite, Mikhail Khokhlov
2015 J jnl
Optimizing regression models for data streams with missing values.
Mach. Learn.
Indre Zliobaite, Jaakko Hollmén
2015 J jnl
Predicting respiratory motion for real-time tumour tracking in radiotherapy.
CoRR
Tomas Krilavicius, Indre Zliobaite, Henrikas Simonavicius, Laimonas Jarusevicius
2015 J jnl
Towards cost-sensitive adaptation: When is it worth updating your predictive model?
Neurocomputing
Indre Zliobaite, Marcin Budka, Frederic T. Stahl
2014 J jnl
A survey on concept drift adaptation.
ACM Comput. Surv.
João Gama, Indre Zliobaite, Albert Bifet, Mykola Pechenizkiy, Abdelhamid Bouchachia
2014 J jnl
Active Learning With Drifting Streaming Data.
IEEE Trans. Neural Networks Learn. Syst.
Indre Zliobaite, Albert Bifet, Bernhard Pfahringer, Geoffrey Holmes
2014 J jnl
Adaptive Preprocessing for Streaming Data.
IEEE Trans. Knowl. Data Eng.
Indre Zliobaite, Bogdan Gabrys
2014 J jnl
Controlled permutations for testing adaptive learning models.
Knowl. Inf. Syst.
Indre Zliobaite
2014 J jnl
Dealing With Concept Drifts in Process Mining.
IEEE Trans. Neural Networks Learn. Syst.
R. P. Jagadeesh Chandra Bose, Wil M. P. van der Aalst, Indre Zliobaite, Mykola Pechenizkiy
2014 B conf
From Sensor Readings to Predictions: On the Process of Developing Practical Soft Sensors.
IDA
Marcin Budka, Mark Eastwood, Bogdan Gabrys, Petr Kadlec, Manuel Martin Salvador, Stephanie Schwan, Athanasios Tsakonas, Indre Zliobaite
2014 — conf
High density-focused uncertainty sampling for active learning over evolving stream data.
BigMine
Dino Ienco, Indre Zliobaite, Bernhard Pfahringer
2014 — conf
Mobile Sensing Data for Urban Mobility Analysis: A Case Study in Preprocessing.
EDBT/ICDT Workshops
Indre Zliobaite, Jaakko Hollmén
2014 B conf
Online Detection of Shutdown Periods in Chemical Plants: A Case Study.
KES
Manuel Martin Salvador, Bogdan Gabrys, Indre Zliobaite
2014 J jnl
Open challenges for data stream mining research.
SIGKDD Explor.
Georg Krempl, Indre Zliobaite, Dariusz Brzezinski, Eyke Hüllermeier, Mark Last, Vincent Lemaire, Tino Noack, Ammar Shaker, Sonja Sievi, Myra Spiliopoulou, Jerzy Stefanowski
2014 J jnl
Towards Hardware-driven Design of Low-energy Algorithms for Data Analysis.
SIGMOD Rec.
Indre Zliobaite, Jaakko Hollmén, Lauri Koskinen, Jukka Teittinen
2013 B conf
CD-MOA: Change Detection Framework for Massive Online Analysis.
IDA
Albert Bifet, Jesse Read, Bernhard Pfahringer, Geoff Holmes, Indre Zliobaite
2013 B conf
Clustering Based Active Learning for Evolving Data Streams.
Discovery Science
Dino Ienco, Albert Bifet, Indre Zliobaite, Bernhard Pfahringer
2013 — ch.
Explainable and Non-explainable Discrimination in Classification.
Discrimination and Privacy in the Information Society
Faisal Kamiran, Indre Zliobaite
2013 — conf
Fault Tolerant Regression for Sensor Data.
ECML/PKDD (1)
Indre Zliobaite, Jaakko Hollmén
2013 J jnl
How good is the Electricity benchmark for evaluating concept drift adaptation
CoRR
Indre Zliobaite
2013 J jnl
Introduction to the special issue on handling concept drift in adaptive information systems.
Evol. Syst.
Mykola Pechenizkiy, Indre Zliobaite
2013 — conf
Pitfalls in Benchmarking Data Stream Classification and How to Avoid Them.
ECML/PKDD (1)
Albert Bifet, Jesse Read, Indre Zliobaite, Bernhard Pfahringer, Geoff Holmes
2013 J jnl
Predictive Handling of Asynchronous Concept Drifts in Distributed Environments.
IEEE Trans. Knowl. Data Eng.
Hock Hee Ang, Vivekanand Gopalkrishnan, Indre Zliobaite, Mykola Pechenizkiy, Steven C. H. Hoi
2013 J jnl
Predictive User Modeling with Actionable Attributes.
CoRR
Indre Zliobaite, Mykola Pechenizkiy
2013 J jnl
Quantifying explainable discrimination and removing illegal discrimination in automated decision making.
Knowl. Inf. Syst.
Faisal Kamiran, Indre Zliobaite, Toon Calders
2013 — ch.
Why Unbiased Computational Processes Can Lead to Discriminative Decision Procedures.
Discrimination and Privacy in the Information Society
Toon Calders, Indre Zliobaite
2012 J jnl
Beating the baseline prediction in food sales: How intelligent an intelligent predictor is?
Expert Syst. Appl.
Indre Zliobaite, Jorn Bakker, Mykola Pechenizkiy
2012 J jnl
Next challenges for adaptive learning systems.
SIGKDD Explor.
Indre Zliobaite, Albert Bifet, Mohamed Medhat Gaber, Bogdan Gabrys, João Gama, Leandro L. Minku, Katarzyna Musial
2012 — conf
Predicting Multi-class Customer Profiles Based on Transactions: a Case Study in Food Sales.
SGAI Conf.
Edward Tersoo Apeh, Indre Zliobaite, Mykola Pechenizkiy, Bogdan Gabrys
2011 — conf
Active Learning with Evolving Streaming Data.
ECML/PKDD (3)
Indre Zliobaite, Albert Bifet, Bernhard Pfahringer, Geoff Holmes
2011 J jnl
Combining similarity in time and space for training set formation under concept drift.
Intell. Data Anal.
Indre Zliobaite
2011 B conf
Context-Aware Personal Route Recognition.
Discovery Science
Oleksiy Mazhelis, Indre Zliobaite, Mykola Pechenizkiy
2011 B conf
Controlled Permutations for Testing Adaptive Classifiers.
Discovery Science
Indre Zliobaite
2011 A conf
Handling Concept Drift in Process Mining.
CAiSE
R. P. Jagadeesh Chandra Bose, Wil M. P. van der Aalst, Indre Zliobaite, Mykola Pechenizkiy
2011 A* conf
Handling Conditional Discrimination.
ICDM
Indre Zliobaite, Faisal Kamiran, Toon Calders
2011 — conf
Identifying Hidden Contexts in Classification.
PAKDD (1)
Indre Zliobaite
2011 — conf
MOA Concept Drift Active Learning Strategies for Streaming Data.
WAPA
Indre Zliobaite, Albert Bifet, Geoff Holmes, Bernhard Pfahringer
2011 — ch.
Three Data Partitioning Strategies for Building Local Classifiers.
Ensembles in Machine Learning Applications
Indre Zliobaite
2010 — conf
Change with Delayed Labeling: When is it Detectable?
ICDM Workshops
Indre Zliobaite
2010 B conf
Handling concept drift in medical applications: Importance, challenges and solutions.
CBMS
Mykola Pechenizkiy, Indre Zliobaite
2010 B conf
Heart failure hospitalization prediction in remote patient management systems.
CBMS
Mykola Pechenizkiy, Ekaterina Vasilyeva, Indre Zliobaite, Aleksandra Tesanovic, Goran Manev
2010 J jnl
Learning under Concept Drift: an Overview
CoRR
Indre Zliobaite
2010 — conf
Learning with Actionable Attributes: Attention -- Boundary Cases!
ICDM Workshops
Indre Zliobaite, Mykola Pechenizkiy
2009 C conf
Combining Time and Space Similarity for Small Size Learning under Concept Drift.
ISMIS
Indre Zliobaite
2009 — conf
Determining the Training Window for Small Sample Size Classification with Concept Drift.
ICDM Workshops
Indre Zliobaite, Ludmila I. Kuncheva
2009 — conf
Handling outliers and concept drift in online mass flow prediction in CFB boilers.
KDD Workshop on Knowledge Discovery from Sensor Data
Jorn Bakker, Mykola Pechenizkiy, Indre Zliobaite, Andriy Ivannikov, Tommi Kärkkäinen
2009 B conf
OMFP: An Approach for Online Mass Flow Prediction in CFB Boilers.
Discovery Science
Indre Zliobaite, Jorn Bakker, Mykola Pechenizkiy
2009 J jnl
On the window size for classification in changing environments.
Intell. Data Anal.
Ludmila I. Kuncheva, Indre Zliobaite
2009 J jnl
Online mass flow prediction in CFB boilers with explicit detection of sudden concept drift.
SIGKDD Explor.
Mykola Pechenizkiy, Jorn Bakker, Indre Zliobaite, Andriy Ivannikov, Tommi Kärkkäinen
2009 — conf
Towards Context Aware Food Sales Prediction.
ICDM Workshops
Indre Zliobaite, Jorn Bakker, Mykola Pechenizkiy
2008 — conf
Expected Classification Error of the Euclidean Linear Classifier under Sudden Concept Drift.
FSKD (2)
Indre Zliobaite
2008 — conf
Linear Discriminant Classifier (LDC) for Streaming Data with Concept Drift.
SSPR/SPR
Ludmila I. Kuncheva, Indre Zliobaite
2007 — conf
Ensemble Learning for Concept Drift Handling - the Role of New Expert.
MLDM Posters
Indre Zliobaite
2006 Misc conf
The Multi-Agent System for Prediction of Financial Time Series.
ICAISC
Sarunas Raudys, Indre Zliobaite
2005 — conf
Prediction of Commodity Prices in Rapidly Changing Environments.
ICAPR (1)
Sarunas Raudys, Indre Zliobaite