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ORCIDIndre Zliobaite
Indrė Žliobaitė
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- unicode name: Indrė Žliobaitė
- affiliation: University of Helsinki, Finland
- affiliation: Aalto University, Department of Information and Computer Science
- affiliation: Helsinki Institute for Information Technology
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2020 – today
- 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- 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
Coauthor Index
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last updated on 2024-05-08 20:55 CEST by the dblp team
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