default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDIndre Zliobaite
Indrė Žliobaitė
2020 – today
- 2024
[j32]Ryoko Noda, Michael Mechenich
, Juha Saarinen, Aki Vehtari, Indre Zliobaite:
Predicting habitat suitability for Asian elephants in non-analog ecosystems with Bayesian models. Ecol. Informatics 82: 102658 (2024)- [c37]Ekaterina Antonenko, Michael Mechenich, Rita Beigaite, Indre Zliobaite, Jesse Read:
Backward Inference in Probabilistic Regressor Chains with Distributional Constraints. IDA (2) 2024: 43-55 - [e9]Albert Bifet, Jesse Davis, Tomas Krilavicius, Meelis Kull, Eirini Ntoutsi, Indre Zliobaite:
Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9-13, 2024, Proceedings, Part I. Lecture Notes in Computer Science 14941, Springer 2024, ISBN 978-3-031-70340-9 [contents] - [e8]Albert Bifet, Jesse Davis, Tomas Krilavicius, Meelis Kull, Eirini Ntoutsi, Indre Zliobaite:
Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9-13, 2024, Proceedings, Part II. Lecture Notes in Computer Science 14942, Springer 2024, ISBN 978-3-031-70343-0 [contents] - [e7]Albert Bifet, Jesse Davis, Tomas Krilavicius, Meelis Kull, Eirini Ntoutsi, Indre Zliobaite:
Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9-13, 2024, Proceedings, Part III. Lecture Notes in Computer Science 14943, Springer 2024, ISBN 978-3-031-70351-5 [contents] - [e6]Albert Bifet, Jesse Davis, Tomas Krilavicius, Meelis Kull, Eirini Ntoutsi, Indre Zliobaite:
Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9-13, 2024, Proceedings, Part IV. Lecture Notes in Computer Science 14944, Springer 2024, ISBN 978-3-031-70358-4 [contents] - [e5]Albert Bifet, Jesse Davis, Tomas Krilavicius, Meelis Kull, Eirini Ntoutsi, Indre Zliobaite:
Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9-13, 2024, Proceedings, Part V. Lecture Notes in Computer Science 14945, Springer 2024, ISBN 978-3-031-70361-4 [contents] - [e4]Albert Bifet, Jesse Davis, Tomas Krilavicius, Meelis Kull, Eirini Ntoutsi, Indre Zliobaite:
Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9-13, 2024, Proceedings, Part VI. Lecture Notes in Computer Science 14946, Springer 2024, ISBN 978-3-031-70364-5 [contents] - [e3]Albert Bifet, Jesse Davis, Tomas Krilavicius, Meelis Kull, Eirini Ntoutsi, Indre Zliobaite:
Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9-13, 2024, Proceedings, Part VII. Lecture Notes in Computer Science 14947, Springer 2024, ISBN 978-3-031-70367-6 [contents] - [e2]Albert Bifet, Povilas Daniusis, Jesse Davis, Tomas Krilavicius, Meelis Kull, Eirini Ntoutsi, Kai Puolamäki, Indre Zliobaite:
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. Lecture Notes in Computer Science 14948, Springer 2024, ISBN 978-3-031-70370-6 [contents] - [i12]Indre Zliobaite:
Modelling the longevity of complex living systems. CoRR abs/2410.02838 (2024) - 2023
- [i11]Indre Zliobaite, Jesse Read:
A Historical Context for Data Streams. CoRR abs/2310.19811 (2023) - 2022
- [j31]Antonio Rafael Sabino Parmezan, Vinícius M. A. de Souza, Arpita Seth, Indre Zliobaite, Gustavo E. A. P. A. Batista:
Hierarchical classification of pollinating flying insects under changing environments. Ecol. Informatics 70: 101751 (2022) - [j30]Rita Beigaite, Jesse Read, Indre Zliobaite:
Multi-output regression with structurally incomplete target labels: A case study of modelling global vegetation cover. Ecol. Informatics 72: 101849 (2022) - [c36]Rita Beigaite, Michael Mechenich, Indre Zliobaite:
Spatial Cross-Validation for Globally Distributed Data. DS 2022: 127-140 - [i10]Jesse Read, Indre Zliobaite:
Learning from Data Streams: An Overview and Update. CoRR abs/2212.14720 (2022) - 2021
- [j29]Esther Galbrun, Hui Tang, Anu Kaakinen, Indre Zliobaite:
Redescription mining for analyzing local limiting conditions: A case study on the biogeography of large mammals in China and southern Asia. Ecol. Informatics 63: 101314 (2021) - [c35]Indre Zliobaite:
Recommender Systems Meet Species Distribution Modelling. Perspectives@RecSys 2021
2010 – 2019
- 2019
- [j28]Indre Zliobaite:
Concept drift over geological times: predictive modeling baselines for analyzing the mammalian fossil record. Data Min. Knowl. Discov. 33(3): 773-803 (2019) - [j27]Indre Zliobaite:
AI minds need to think about energy constraints. Nat. Mach. Intell. 1(8): 335 (2019) - 2018
- [j26]Roelant A. Stegmann, Indre Zliobaite, Tuukka Tolvanen, Jaakko Hollmén, Jesse Read:
A survey of evaluation methods for personal route and destination prediction from mobility traces. WIREs Data Mining Knowl. Discov. 8(2) (2018) - 2017
- [j25]Indre Zliobaite:
Measuring discrimination in algorithmic decision making. Data Min. Knowl. Discov. 31(4): 1060-1089 (2017) - [j24]Jukka Teittinen, Markus Hiienkari, Indre Zliobaite, Jaakko Hollmén, Heikki Berg, Juha Heiskala, Timo Viitanen, Jesse Simonsson, Lauri Koskinen:
A 5.3 pJ/op approximate TTA VLIW tailored for machine learning. Microelectron. J. 61: 106-113 (2017) - [c34]Alexandr V. Maslov, Mykola Pechenizkiy, Yulong Pei, Indre Zliobaite, Alexander Shklyaev, Tommi Kärkkäinen, Jaakko Hollmén:
BLPA: Bayesian learn-predict-adjust method for online detection of recurrent changepoints. IJCNN 2017: 1916-1923 - [e1]Ricard Gavaldà, Indre Zliobaite, João Gama:
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. CEUR Workshop Proceedings 1831, CEUR-WS.org 2017 [contents] - [i9]Indre Zliobaite:
Fairness-aware machine learning: a perspective. CoRR abs/1708.00754 (2017) - 2016
- [j23]Indre Zliobaite, Bart Custers:
Using sensitive personal data may be necessary for avoiding discrimination in data-driven decision models. Artif. Intell. Law 24(2): 183-201 (2016) - [j22]Indre Zliobaite, Mikhail Khokhlov:
Optimal estimates for short horizon travel time prediction in urban areas. Intell. Data Anal. 20(6): 1459-1475 (2016) - [j21]Jesse Read, Indre Zliobaite, Jaakko Hollmén:
Labeling sensing data for mobility modeling. Inf. Syst. 57: 207-222 (2016) - [c33]Tomas Krilavicius, Indre Zliobaite, Henrikas Simonavicius, Laimonas Jarusevicius:
Predicting Respiratory Motion for Real-Time Tumour Tracking in Radiotherapy. CBMS 2016: 7-12 - [c32]Alexandr V. Maslov, Mykola Pechenizkiy, Indre Zliobaite, Tommi Kärkkäinen:
Modelling Recurrent Events for Improving Online Change Detection. SDM 2016: 549-557 - [i8]Indre Zliobaite, Nikolaj Tatti:
A note on adjusting $R^2$ for using with cross-validation. CoRR abs/1605.01703 (2016) - 2015
- [j20]Concha Bielza, João Gama, Alípio Jorge, Indre Zliobaite:
Guest editors introduction: special issue of the ECMLPKDD 2015 journal track. Data Min. Knowl. Discov. 29(5): 1113-1115 (2015) - [j19]Indre Zliobaite, Marcin Budka, Frederic T. Stahl:
Towards cost-sensitive adaptation: When is it worth updating your predictive model? Neurocomputing 150: 240-249 (2015) - [j18]Indre Zliobaite, Albert Bifet, Jesse Read, Bernhard Pfahringer, Geoff Holmes:
Evaluation methods and decision theory for classification of streaming data with temporal dependence. Mach. Learn. 98(3): 455-482 (2015) - [j17]Indre Zliobaite, Jaakko Hollmén:
Optimizing regression models for data streams with missing values. Mach. Learn. 99(1): 47-73 (2015) - [j16]Concha Bielza, João Gama, Alípio Jorge, Indre Zliobaite:
Guest Editors introduction: special issue of the ECMLPKDD 2015 journal track. Mach. Learn. 100(2-3): 157-159 (2015) - [c31]Indre Zliobaite, Michael Mathioudakis, Tuukka Lehtiniemi, Pekka Parviainen, Tomi Janhunen:
Accessibility by Public Transport Predicts Residential Real Estate Prices: A Case Study in Helsinki Region. MUD@ICML 2015: 65-71 - [i7]Indre Zliobaite:
On the relation between accuracy and fairness in binary classification. CoRR abs/1505.05723 (2015) - [i6]Indre Zliobaite, Mikhail Khokhlov:
Optimal estimates for short horizon travel time prediction in urban areas. CoRR abs/1507.08444 (2015) - [i5]Tomas Krilavicius, Indre Zliobaite, Henrikas Simonavicius, Laimonas Jarusevicius:
Predicting respiratory motion for real-time tumour tracking in radiotherapy. CoRR abs/1508.00749 (2015) - [i4]Indre Zliobaite:
A survey on measuring indirect discrimination in machine learning. CoRR abs/1511.00148 (2015) - 2014
- [j15]João Gama, Indre Zliobaite, Albert Bifet, Mykola Pechenizkiy, Abdelhamid Bouchachia:
A survey on concept drift adaptation. ACM Comput. Surv. 46(4): 44:1-44:37 (2014) - [j14]Indre Zliobaite:
Controlled permutations for testing adaptive learning models. Knowl. Inf. Syst. 39(3): 565-578 (2014) - [j13]Georg Krempl, Indre Zliobaite, Dariusz Brzezinski, Eyke Hüllermeier, Mark Last, Vincent Lemaire, Tino Noack, Ammar Shaker, Sonja Sievi, Myra Spiliopoulou, Jerzy Stefanowski:
Open challenges for data stream mining research. SIGKDD Explor. 16(1): 1-10 (2014) - [j12]Indre Zliobaite, Jaakko Hollmén, Lauri Koskinen, Jukka Teittinen:
Towards Hardware-driven Design of Low-energy Algorithms for Data Analysis. SIGMOD Rec. 43(4): 15-20 (2014) - [j11]Indre Zliobaite, Bogdan Gabrys:
Adaptive Preprocessing for Streaming Data. IEEE Trans. Knowl. Data Eng. 26(2): 309-321 (2014) - [j10]Indre Zliobaite, Albert Bifet, Bernhard Pfahringer, Geoffrey Holmes:
Active Learning With Drifting Streaming Data. IEEE Trans. Neural Networks Learn. Syst. 25(1): 27-39 (2014) - [j9]R. P. Jagadeesh Chandra Bose, Wil M. P. van der Aalst, Indre Zliobaite, Mykola Pechenizkiy:
Dealing With Concept Drifts in Process Mining. IEEE Trans. Neural Networks Learn. Syst. 25(1): 154-171 (2014) - [c30]Indre Zliobaite, Jaakko Hollmén:
Mobile Sensing Data for Urban Mobility Analysis: A Case Study in Preprocessing. EDBT/ICDT Workshops 2014: 309-314 - [c29]Marcin Budka, Mark Eastwood, Bogdan Gabrys, Petr Kadlec, Manuel Martin Salvador, Stephanie Schwan, Athanasios Tsakonas, Indre Zliobaite:
From Sensor Readings to Predictions: On the Process of Developing Practical Soft Sensors. IDA 2014: 49-60 - [c28]Dino Ienco, Indre Zliobaite, Bernhard Pfahringer:
High density-focused uncertainty sampling for active learning over evolving stream data. BigMine 2014: 133-148 - [c27]Manuel Martin Salvador, Bogdan Gabrys, Indre Zliobaite:
Online Detection of Shutdown Periods in Chemical Plants: A Case Study. KES 2014: 580-588 - 2013
- [j8]Mykola Pechenizkiy, Indre Zliobaite:
Introduction to the special issue on handling concept drift in adaptive information systems. Evol. Syst. 4(1): 1-2 (2013) - [j7]Faisal Kamiran, Indre Zliobaite, Toon Calders:
Quantifying explainable discrimination and removing illegal discrimination in automated decision making. Knowl. Inf. Syst. 35(3): 613-644 (2013) - [j6]Hock Hee Ang, Vivekanand Gopalkrishnan, Indre Zliobaite, Mykola Pechenizkiy, Steven C. H. Hoi:
Predictive Handling of Asynchronous Concept Drifts in Distributed Environments. IEEE Trans. Knowl. Data Eng. 25(10): 2343-2355 (2013) - [c26]Dino Ienco, Albert Bifet, Indre Zliobaite, Bernhard Pfahringer:
Clustering Based Active Learning for Evolving Data Streams. Discovery Science 2013: 79-93 - [c25]Albert Bifet, Jesse Read, Bernhard Pfahringer, Geoff Holmes, Indre Zliobaite:
CD-MOA: Change Detection Framework for Massive Online Analysis. IDA 2013: 92-103 - [c24]Indre Zliobaite, Jaakko Hollmén:
Fault Tolerant Regression for Sensor Data. ECML/PKDD (1) 2013: 449-464 - [c23]Albert Bifet, Jesse Read, Indre Zliobaite, Bernhard Pfahringer, Geoff Holmes:
Pitfalls in Benchmarking Data Stream Classification and How to Avoid Them. ECML/PKDD (1) 2013: 465-479 - [p3]Toon Calders, Indre Zliobaite:
Why Unbiased Computational Processes Can Lead to Discriminative Decision Procedures. Discrimination and Privacy in the Information Society 2013: 43-57 - [p2]Faisal Kamiran, Indre Zliobaite:
Explainable and Non-explainable Discrimination in Classification. Discrimination and Privacy in the Information Society 2013: 155-170 - [i3]Indre Zliobaite:
How good is the Electricity benchmark for evaluating concept drift adaptation. CoRR abs/1301.3524 (2013) - [i2]Indre Zliobaite, Mykola Pechenizkiy:
Predictive User Modeling with Actionable Attributes. CoRR abs/1312.6558 (2013) - 2012
- [j5]Indre Zliobaite, Jorn Bakker, Mykola Pechenizkiy:
Beating the baseline prediction in food sales: How intelligent an intelligent predictor is? Expert Syst. Appl. 39(1): 806-815 (2012) - [j4]Indre Zliobaite, Albert Bifet, Mohamed Medhat Gaber, Bogdan Gabrys, João Gama, Leandro L. Minku, Katarzyna Musial:
Next challenges for adaptive learning systems. SIGKDD Explor. 14(1): 48-55 (2012) - [c22]Edward Tersoo Apeh, Indre Zliobaite, Mykola Pechenizkiy, Bogdan Gabrys:
Predicting Multi-class Customer Profiles Based on Transactions: a Case Study in Food Sales. SGAI Conf. 2012: 213-218 - 2011
- [j3]Indre Zliobaite:
Combining similarity in time and space for training set formation under concept drift. Intell. Data Anal. 15(4): 589-611 (2011) - [c21]R. P. Jagadeesh Chandra Bose, Wil M. P. van der Aalst, Indre Zliobaite, Mykola Pechenizkiy:
Handling Concept Drift in Process Mining. CAiSE 2011: 391-405 - [c20]Oleksiy Mazhelis, Indre Zliobaite, Mykola Pechenizkiy:
Context-Aware Personal Route Recognition. Discovery Science 2011: 221-235 - [c19]Indre Zliobaite:
Controlled Permutations for Testing Adaptive Classifiers. Discovery Science 2011: 365-379 - [c18]Indre Zliobaite, Faisal Kamiran, Toon Calders:
Handling Conditional Discrimination. ICDM 2011: 992-1001 - [c17]Indre Zliobaite:
Identifying Hidden Contexts in Classification. PAKDD (1) 2011: 277-288 - [c16]Indre Zliobaite, Albert Bifet, Bernhard Pfahringer, Geoff Holmes:
Active Learning with Evolving Streaming Data. ECML/PKDD (3) 2011: 597-612 - [c15]Indre Zliobaite, Albert Bifet, Geoff Holmes, Bernhard Pfahringer:
MOA Concept Drift Active Learning Strategies for Streaming Data. WAPA 2011: 48-55 - [p1]Indre Zliobaite:
Three Data Partitioning Strategies for Building Local Classifiers. Ensembles in Machine Learning Applications 2011: 233-250 - 2010
- [c14]Mykola Pechenizkiy, Indre Zliobaite:
Handling concept drift in medical applications: Importance, challenges and solutions. CBMS 2010: 5 - [c13]Mykola Pechenizkiy, Ekaterina Vasilyeva, Indre Zliobaite, Aleksandra Tesanovic, Goran Manev:
Heart failure hospitalization prediction in remote patient management systems. CBMS 2010: 44-49 - [c12]Indre Zliobaite:
Change with Delayed Labeling: When is it Detectable? ICDM Workshops 2010: 843-850 - [c11]Indre Zliobaite, Mykola Pechenizkiy:
Learning with Actionable Attributes: Attention -- Boundary Cases! ICDM Workshops 2010: 1021-1028 - [i1]Indre Zliobaite:
Learning under Concept Drift: an Overview. CoRR abs/1010.4784 (2010)
2000 – 2009
- 2009
- [j2]Ludmila I. Kuncheva, Indre Zliobaite:
On the window size for classification in changing environments. Intell. Data Anal. 13(6): 861-872 (2009) - [j1]Mykola Pechenizkiy, Jorn Bakker, Indre Zliobaite, Andriy Ivannikov, Tommi Kärkkäinen:
Online mass flow prediction in CFB boilers with explicit detection of sudden concept drift. SIGKDD Explor. 11(2): 109-116 (2009) - [c10]Indre Zliobaite, Jorn Bakker, Mykola Pechenizkiy:
OMFP: An Approach for Online Mass Flow Prediction in CFB Boilers. Discovery Science 2009: 272-286 - [c9]Indre Zliobaite, Jorn Bakker, Mykola Pechenizkiy:
Towards Context Aware Food Sales Prediction. ICDM Workshops 2009: 94-99 - [c8]Indre Zliobaite, Ludmila I. Kuncheva:
Determining the Training Window for Small Sample Size Classification with Concept Drift. ICDM Workshops 2009: 447-452 - [c7]Indre Zliobaite:
Combining Time and Space Similarity for Small Size Learning under Concept Drift. ISMIS 2009: 412-421 - [c6]Jorn Bakker, Mykola Pechenizkiy, Indre Zliobaite, Andriy Ivannikov, Tommi Kärkkäinen:
Handling outliers and concept drift in online mass flow prediction in CFB boilers. KDD Workshop on Knowledge Discovery from Sensor Data 2009: 13-22 - 2008
- [c5]Indre Zliobaite:
Expected Classification Error of the Euclidean Linear Classifier under Sudden Concept Drift. FSKD (2) 2008: 29-33 - [c4]Ludmila I. Kuncheva, Indre Zliobaite:
Linear Discriminant Classifier (LDC) for Streaming Data with Concept Drift. SSPR/SPR 2008: 4 - 2007
- [c3]Indre Zliobaite:
Ensemble Learning for Concept Drift Handling - the Role of New Expert. MLDM Posters 2007: 251-260 - 2006
- [c2]Sarunas Raudys, Indre Zliobaite:
The Multi-Agent System for Prediction of Financial Time Series. ICAISC 2006: 653-662 - 2005
- [c1]Sarunas Raudys, Indre Zliobaite:
Prediction of Commodity Prices in Rapidly Changing Environments. ICAPR (1) 2005: 154-163
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
[+][–] Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
[+][–] Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2025-03-04 22:11 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
