Conformal prediction for out-of-distribution time-series classification.

Krisztián Búza Krisztian Antal Buza Hungarian Academy of Sciences, Budapest, Hungary Semmelweis University, Budapest, Hungary University of Hildesheim, Germany https://orcid.org/0000-0002-7111-6452 https://www.wikidata.org/entity/Q59875460

Alexandros Nanopoulos Krisztián Búza Conformal prediction for out-of-distribution time-series classification. 823 2025 July 55 Appl. Intell. 11 https://doi.org/10.1007/s10489-025-06708-7 db/journals/apin/apin55.html#NanopoulosB25 streams/journals/apin

Kamil Ksiazek Wilhelm Masarczyk Przemyslaw Glomb Michal Romaszewski Krisztián Búza Przemyslaw Sekula Michal Cholewa Katarzyna Kolodziej Piotr Gorczyca Magdalena Piegza Deep learning approach for automatic assessment of schizophrenia and bipolar disorder in patients using R-R intervals. 1012983 2025 21 PLoS Comput. Biol. 9 https://doi.org/10.1371/journal.pcbi.1012983 db/journals/ploscb/ploscb21.html#KsiazekMGRBSCKGP25 streams/journals/ploscb

Kamil Ksiazek Wilhelm Masarczyk Przemyslaw Glomb Michal Romaszewski Iga Stoklosa Piotr Scislo Pawel Debski Robert Pudlo Krisztián Búza Piotr Gorczyca Magdalena Piegza Assessment of symptom severity in psychotic disorder patients based on heart rate variability and accelerometer mobility data. 108544 2024 176 Comput. Biol. Medicine https://doi.org/10.1016/j.compbiomed.2024.108544 db/journals/cbm/cbm176.html#KsiazekMGRSSDPBGP24

Margit Antal Krisztián Búza Szilárd Nemes Advancements in Text Subjectivity Analysis: From Simple Approaches to BERT-Based Models and Generalization Assessments. 245-255 2024 ICCCI (CCIS Volume 1) https://doi.org/10.1007/978-3-031-70248-8_19 conf/iccci/2024ccis1 db/conf/iccci/iccci2024ccis1.html#AntalBN24 Krisztián Búza Margit Antal ROCKET with Dynamic Convolution for Time Series Classification. 271-282 2024 ICCCI (CCIS Volume 1) https://doi.org/10.1007/978-3-031-70248-8_21 conf/iccci/2024ccis1 db/conf/iccci/iccci2024ccis1.html#BuzaA24 Krisztián Búza Activity Recognition Based on Accelerometer Data with Enhanced ROCKET Algorithm. 321-326 2024 SACI https://doi.org/10.1109/SACI60582.2024.10619836 conf/saci/2024 db/conf/saci/saci2024.html#Buza24 Krisztián Búza Sparsity-Invariant Convolution for Forecasting Irregularly Sampled Time Series. 151-162 2023 ICCCI https://doi.org/10.1007/978-3-031-41456-5_12 conf/iccci/2023 db/conf/iccci/iccci2023.html#Buza23 Krisztián Búza Kamil Ksiazek Wilhelm Masarczyk Przemyslaw Glomb Piotr Gorczyca Magdalena Piegza A Simple and Effective Classifier for the Detection of Schizophrenia and Bipolar Disorder based on Heart Rate Variability Time Series. 217-222 2023 ITAT https://ceur-ws.org/Vol-3498/paper28.pdf conf/itat/2023 db/conf/itat/itat2023.html#BuzaKMGGP23 Bartosz Grabowski Przemyslaw Glomb Kamil Ksiazek Krisztián Búza Improving Autoencoders Performance for Hyperspectral Unmixing Using Clustering. 102-121 2022 ACIIDS (Companion) https://doi.org/10.1007/978-981-19-8234-7_9 conf/aciids/2022c db/conf/aciids/aciids2022c.html#GrabowskiGKB22 Kamil Ksiazek Przemyslaw Glomb Michal Romaszewski Michal Cholewa Bartosz Grabowski Krisztián Búza Improving Autoencoder Training Performance for Hyperspectral Unmixing with Network Reinitialisation. 391-403 2022 ICIAP (1) https://doi.org/10.1007/978-3-031-06427-2_33 conf/iciap/2022-1 db/conf/iciap/iciap2022-1.html#KsiazekGRCGB22 Ladislav Peska Patrik Veselý Tomás Skopal Krisztián Búza Person Authentication using Visual Representations of Keyboard Typing Dynamics. 1-6 2022 SNAMS https://doi.org/10.1109/SNAMS58071.2022.10062739 conf/snams/2022 db/conf/snams/snams2022.html#PeskaVSB22

Margit Antal Krisztián Búza Norbert Fejér SapiAgent: A Bot Based on Deep Learning to Generate Human-Like Mouse Trajectories. 124396-124408 2021 9 IEEE Access https://doi.org/10.1109/ACCESS.2021.3111098 db/journals/access/access9.html#AntalBF21

Krisztián Búza Margit Antal Convolutional Neural Networks with Dynamic Convolution for Time Series Classification. 304-312 2021 ICCCI (CCIS Volume) https://doi.org/10.1007/978-3-030-88113-9_24 conf/iccci/2021ccis db/conf/iccci/iccci2021ccis.html#BuzaA21 Margit Antal Norbert Fejér Krisztián Búza SapiMouse: Mouse Dynamics-based User Authentication Using Deep Feature Learning. 61-66 2021 SACI https://doi.org/10.1109/SACI51354.2021.9465583 conf/saci/2021 db/conf/saci/saci2021.html#AntalFB21 Anita-Bella Réthi Krisztian Antal Buza Orsolya Máthé Mátyás Fosztó Tamás Koncz Károly Simon medR: Software System for Managing Medical History and Patient Examination Data. 61-66 2021 SISY https://doi.org/10.1109/SISY52375.2021.9582491 conf/sisy/2021 db/conf/sisy/sisy2021.html#RethiBMFKS21

Aleksandra Revina Krisztián Búza Vera G. Meister IT Ticket Classification: The Simpler, the Better. 193380-193395 2020 8 IEEE Access https://doi.org/10.1109/ACCESS.2020.3032840 db/journals/access/access8.html#RevinaBM20

Krisztián Búza Aleksandra Revina Speeding up the SUCCESS Approach for Massive Industrial Datasets. 1-6 2020 INISTA https://doi.org/10.1109/INISTA49547.2020.9194656 conf/inista/2020 db/conf/inista/inista2020.html#BuzaR20 Krisztián Búza ASTERICS: Projection-based Classification of EEG with Asymmetric Loss Linear Regression and Genetic Algorithm. 35-40 2020 SACI https://doi.org/10.1109/SACI49304.2020.9118837 conf/saci/2020 db/conf/saci/saci2020.html#Buza20 Krisztián Búza Tomás Horváth Factorization Machines for Blog Feedback Prediction. 79-85 2019 CORES https://doi.org/10.1007/978-3-030-19738-4_9 conf/cores/2019 db/conf/cores/cores2019.html#BuzaH19 Zakarya Farou Krisztián Búza The Warping Window Size Effects the Accuracy of Person Identification based on Keystroke Dynamics. 84-89 2019 ITAT https://ceur-ws.org/Vol-2473/paper2.pdf conf/itat/2019 db/conf/itat/itat2019.html#FarouB19 Dániel T. Várkonyi Krisztián Búza Extreme Learning Machines with Regularization for the Classification of Gene Expression Data. 99-103 2019 ITAT https://ceur-ws.org/Vol-2473/paper11.pdf conf/itat/2019 db/conf/itat/itat2019.html#VarkonyiB19

Krisztián Búza Encouraging an Appropriate Representation Simplifies Training of Neural Networks. 2019 abs/1911.07245 CoRR http://arxiv.org/abs/1911.07245 db/journals/corr/corr1911.html#abs-1911-07245

Krisztián Búza Time Series Classification and its Applications. 4:1-4:4 2018 WIMS https://doi.org/10.1145/3227609.3227690 conf/wims/2018 db/conf/wims/wims2018.html#Buza18 Annamária Szenkovits Regina Meszlényi Krisztián Búza Noémi Gaskó Rodica Ioana Lung Mihai Suciu 0001 Feature Selection with a Genetic Algorithm for Classification of Brain Imaging Data. 185-202 2018 Advances in Feature Selection for Data and Pattern Recognition https://doi.org/10.1007/978-3-319-67588-6_10 series/isrl/138 db/series/isrl/isrl138.html#SzenkovitsMBGLS18

Ladislav Peska Krisztián Búza Júlia Koller Drug-target interaction prediction: A Bayesian ranking approach. 15-21 2017 152 Comput. Methods Programs Biomed. https://doi.org/10.1016/j.cmpb.2017.09.003 https://www.wikidata.org/entity/Q45944765 db/journals/cmpb/cmpb152.html#PeskaBK17

Regina Meszlényi Krisztián Búza Zoltán Vidnyánszky Resting State fMRI Functional Connectivity-Based Classification Using a Convolutional Neural Network Architecture. 2017 11 Frontiers Neuroinformatics https://doi.org/10.3389/fninf.2017.00061 https://www.wikidata.org/entity/Q45944302 db/journals/fini/fini11.html#MeszlenyiBV17 61

Krisztián Búza Ladislav Peska Drug-target interaction prediction with Bipartite Local Models and hubness-aware regression. 284-293 2017 260 Neurocomputing https://doi.org/10.1016/j.neucom.2017.04.055 db/journals/ijon/ijon260.html#BuzaP17

Krisztián Búza Piroska B. Kis Towards Privacy-Aware Keyboards. 140-147 2017 CORES https://doi.org/10.1007/978-3-319-59162-9_15 conf/cores/2017 db/conf/cores/cores2017.html#BuzaK17 Dóra Neubrandt Krisztián Búza Projection-Based Person Identification. 221-228 2017 CORES https://doi.org/10.1007/978-3-319-59162-9_23 conf/cores/2017 db/conf/cores/cores2017.html#NeubrandtB17 Rajkumar Ramamurthy Christian Bauckhage Krisztián Búza Stefan Wrobel Using Echo State Networks for Cryptography. 663-671 2017 ICANN (2) https://doi.org/10.1007/978-3-319-68612-7_75 https://www.wikidata.org/entity/Q58624263 conf/icann/2017-2 db/conf/icann/icann2017-2.html#RamamurthyBBW17 Krisztián Búza Ladislav Peska ALADIN: A New Approach for Drug-Target Interaction Prediction. 322-337 2017 ECML/PKDD (2) https://doi.org/10.1007/978-3-319-71246-8_20 conf/pkdd/2017-2 db/conf/pkdd/pkdd2017-2.html#BuzaP17

Rajkumar Ramamurthy Christian Bauckhage Krisztián Búza Stefan Wrobel Using Echo State Networks for Cryptography. 2017 abs/1704.01046 CoRR http://arxiv.org/abs/1704.01046 db/journals/corr/corr1704.html#RamamurthyBBW17

Regina Meszlényi Krisztián Búza Zoltán Vidnyánszky Resting state fMRI functional connectivity-based classification using a convolutional neural network architecture. 2017 abs/1707.06682 CoRR http://arxiv.org/abs/1707.06682 db/journals/corr/corr1707.html#MeszlenyiBV17

Krisztián Búza Noémi Ágnes Varga ParkinsoNET: Estimation of UPDRS Score Using Hubness-Aware Feedforward Neural Networks. 541-555 2016 30 Appl. Artif. Intell. 6 https://doi.org/10.1080/08839514.2016.1193716 https://www.wikidata.org/entity/Q58309091 db/journals/aai/aai30.html#BuzaV16

Krisztián Búza Classification of gene expression data: A hubness-aware semi-supervised approach. 105-113 2016 127 Comput. Methods Programs Biomed. https://doi.org/10.1016/j.cmpb.2016.01.016 https://www.wikidata.org/entity/Q31061445 db/journals/cmpb/cmpb127.html#Buza16

Nenad Tomasev Krisztián Búza Dunja Mladenic Correcting the hub occurrence prediction bias in many dimensions. 1-21 2016 13 Comput. Sci. Inf. Syst. 1 https://doi.org/10.2298/CSIS140929039T https://www.wikidata.org/entity/Q62718784 db/journals/comsis/comsis13.html#TomasevBM16

Krisztián Búza Person Identification Based on Keystroke Dynamics: Demo and Open Challenge. 161-168 2016 CAiSE Forum https://ceur-ws.org/Vol-1612/paper21.pdf conf/caise/2016fo db/conf/caise/caisefo2016.html#Buza16 Regina Meszlényi Ladislav Peska Viktor Gál Zoltán Vidnyánszky Krisztián Búza A Model for Classification Based on the Functional Connectivity Pattern Dynamics of the Brain. 203-208 2016 ENIC https://doi.org/10.1109/ENIC.2016.037 https://doi.ieeecomputersociety.org/10.1109/ENIC.2016.037 conf/enic/2016 db/conf/enic/enic2016.html#MeszlenyiPGVB16 Regina Meszlényi Ladislav Peska Viktor Gál Zoltán Vidnyánszky Krisztián Búza Classification of fMRI data using dynamic time warping based functional connectivity analysis. 245-249 2016 EUSIPCO https://doi.org/10.1109/EUSIPCO.2016.7760247 conf/eusipco/2016 db/conf/eusipco/eusipco2016.html#MeszlenyiPGVB16 Rodica Ioana Lung Mihai Suciu 0001 Regina Meszlényi Krisztián Búza Noémi Gaskó Community Structure Detection for the Functional Connectivity Networks of the Brain. 633-643 2016 PPSN https://doi.org/10.1007/978-3-319-45823-6_59 conf/ppsn/2016 db/conf/ppsn/ppsn2016.html#LungSMBG16 Krisztián Búza Drug-target interaction prediction with hubness-aware machine learning. 437-440 2016 SACI https://doi.org/10.1109/SACI.2016.7507416 conf/saci/2016 db/conf/saci/saci2016.html#Buza16 Krisztián Búza Dóra Neubrandt How you type is who you are. 453-456 2016 SACI https://doi.org/10.1109/SACI.2016.7507419 conf/saci/2016 db/conf/saci/saci2016.html#BuzaN16

Nenad Tomasev Krisztián Búza Hubness-aware kNN classification of high-dimensional data in presence of label noise. 157-172 2015 160 Neurocomputing https://doi.org/10.1016/j.neucom.2014.10.084 https://www.wikidata.org/entity/Q62718790 db/journals/ijon/ijon160.html#TomasevB15

Krisztián Búza Alexandros Nanopoulos Gábor I. Nagy Nearest neighbor regression in the presence of bad hubs. 250-260 2015 86 Knowl. Based Syst. https://doi.org/10.1016/j.knosys.2015.06.010 db/journals/kbs/kbs86.html#BuzaNN15

Krisztián Búza Semi-supervised Naive Hubness Bayesian k-Nearest Neighbor for Gene Expression Data. 101-110 2015 CORES https://doi.org/10.1007/978-3-319-26227-7_10 conf/cores/2015 db/conf/cores/cores2015.html#Buza15 Krisztián Búza Júlia Koller Kristóf Marussy PROCESS: Projection-Based Classification of Electroencephalograph Signals. 91-100 2015 ICAISC https://doi.org/10.1007/978-3-319-19369-4_9 conf/icaisc/2015-2 db/conf/icaisc/icaisc2015-2.html#BuzaKM15 Nenad Tomasev Krisztián Búza Kristóf Marussy Piroska B. Kis Hubness-Aware Classification, Instance Selection and Feature Construction: Survey and Extensions to Time-Series. 231-262 2015 Feature Selection for Data and Pattern Recognition https://doi.org/10.1007/978-3-662-45620-0_11 https://www.wikidata.org/entity/Q62718807 series/sci/2015-584 db/series/sci/sci584.html#TomasevBMK15

Krisztián Búza Gábor I. Nagy Alexandros Nanopoulos Storage-optimizing clustering algorithms for high-dimensional tick data. 4148-4157 2014 41 Expert Syst. Appl. 9 https://doi.org/10.1016/j.eswa.2013.12.046 db/journals/eswa/eswa41.html#BuzaNN14

Krisztián Búza BlogFeedback. 2014 May https://doi.org/10.24432/C58S3F UCI-MLR streams/repo/ucimlr Kristóf Marussy Krisztián Búza SUCCESS: A New Approach for Semi-supervised Classification of Time-Series. 437-447 2013 ICAISC (1) https://doi.org/10.1007/978-3-642-38658-9_39 conf/icaisc/2013-1 db/conf/icaisc/icaisc2013-1.html#MarussyB13

Krisztián Búza Alexandros Nanopoulos Tomás Horváth Lars Schmidt-Thieme GRAMOFON: General model-selection framework based on networks. 163-170 2012 75 Neurocomputing 1 https://doi.org/10.1016/j.neucom.2011.02.026 https://www.wikidata.org/entity/Q59866359 db/journals/ijon/ijon75.html#BuzaNHS12

Krisztián Búza Feedback Prediction for Blogs. 145-152 2012 GfKl https://doi.org/10.1007/978-3-319-01595-8_16 conf/gfkl/2012 db/conf/gfkl/gfkl2012.html#Buza12 Gábor I. Nagy Krisztián Búza SOHAC: Efficient Storage of Tick Data That Supports Search and Analysis. 38-51 2012 ICDM https://doi.org/10.1007/978-3-642-31488-9_4 conf/incdm/2012 db/conf/incdm/incdm2012.html#NagyB12 Krisztian Antal Buza Fusion Methods for Time-Series Classification. 2011 University of Hildesheim 1-144 978-3-631-63085-3 https://d-nb.info/1016473028 Krisztián Búza Alexandros Nanopoulos Lars Schmidt-Thieme IQ estimation for accurate time-series classification. 216-223 2011 CIDM https://doi.org/10.1109/CIDM.2011.5949441 https://www.wikidata.org/entity/Q59866431 conf/cidm/2011 db/conf/cidm/cidm2011.html#BuzaNS11 Krisztián Búza Alexandros Nanopoulos Lars Schmidt-Thieme Fusion of Similarity Measures for Time Series Classification. 253-261 2011 HAIS (2) https://doi.org/10.1007/978-3-642-21222-2_31 https://www.wikidata.org/entity/Q59866426 conf/hais/2011-2 db/conf/hais/hais2011-2.html#BuzaNS11 Krisztián Búza Alexandros Nanopoulos Lars Schmidt-Thieme Júlia Koller Fast Classification of Electrocardiograph Signals via Instance Selection. 9-16 2011 HISB https://doi.org/10.1109/HISB.2011.26 https://doi.ieeecomputersociety.org/10.1109/HISB.2011.26 https://www.wikidata.org/entity/Q59866419 conf/hisb/2011 db/conf/hisb/hisb2011.html#BuzaNSK11 Krisztián Búza Antal Buza Piroska B. Kis A Distributed Genetic Algorithm for Graph-Based Clustering. 323-331 2011 ICMMI https://doi.org/10.1007/978-3-642-23169-8_35 conf/icmmi/2011 db/conf/icmmi/icmmi2011.html#BuzaBK11 Timo Reuter Philipp Cimiano Lucas Drumond Krisztián Búza Lars Schmidt-Thieme Scalable Event-Based Clustering of Social Media Via Record Linkage Techniques. 2011 ICWSM http://www.aaai.org/ocs/index.php/ICWSM/ICWSM11/paper/view/2760 conf/icwsm/2011 db/conf/icwsm/icwsm2011.html#ReuterCDBS11 Krisztián Búza Alexandros Nanopoulos Lars Schmidt-Thieme INSIGHT: Efficient and Effective Instance Selection for Time-Series Classification. 149-160 2011 PAKDD (2) https://doi.org/10.1007/978-3-642-20847-8_13 conf/pakdd/2011-2 db/conf/pakdd/pakdd2011-2.html#BuzaNS11 Krisztián Búza Alexandros Nanopoulos Lars Schmidt-Thieme Time-Series Classification Based on Individualised Error Prediction. 48-54 2010 CSE https://doi.org/10.1109/CSE.2010.16 https://doi.ieeecomputersociety.org/10.1109/CSE.2010.16 https://www.wikidata.org/entity/Q59866500 conf/cse/2010 db/conf/cse/cse2010.html#BuzaNS10 Krisztián Búza Alexandros Nanopoulos Lars Schmidt-Thieme Individualized Error Estimation for Classification and Regression Models. 183-191 2010 GfKl https://doi.org/10.1007/978-3-642-24466-7_19 conf/gfkl/2010 db/conf/gfkl/gfkl2010.html#BuzaNS10 Krisztián Búza Alexandros Nanopoulos Lars Schmidt-Thieme Graph-Based Model-Selection Framework for Large Ensembles. 557-564 2010 HAIS (1) https://doi.org/10.1007/978-3-642-13769-3_68 https://www.wikidata.org/entity/Q59866467 conf/hais/2010-1 db/conf/hais/hais2010-1.html#BuzaNS10 Krisztián Búza Lars Schmidt-Thieme Motif-Based Classification of Time Series with Bayesian Networks and SVMs. 105-114 2008 GfKl https://doi.org/10.1007/978-3-642-01044-6_9 conf/gfkl/2008 db/conf/gfkl/gfkl2008.html#BuzaS08 Leandro Balby Marinho Krisztián Búza Lars Schmidt-Thieme Folksonomy-Based Collabulary Learning. 261-276 2008 ISWC https://doi.org/10.1007/978-3-540-88564-1_17 https://www.wikidata.org/entity/Q125463246 conf/semweb/2008 db/conf/semweb/iswc2008.html#MarinhoBS08 Margit Antal Christian Bauckhage Antal Buza Michal Cholewa Philipp Cimiano Pawel Debski Lucas Drumond Zakarya Farou Norbert Fejér Mátyás Fosztó Viktor Gál Noémi Gaskó Przemyslaw Glomb Piotr Gorczyca Bartosz Grabowski Tomás Horváth Piroska B. Kis Júlia Koller Katarzyna Kolodziej Tamás Koncz Kamil Ksiazek Rodica Ioana Lung Leandro Balby Marinho Kristóf Marussy Wilhelm Masarczyk Orsolya Máthé Vera G. Meister Regina Meszlényi Dunja Mladenic Gábor I. Nagy Alexandros Nanopoulos Szilárd Nemes Dóra Neubrandt Ladislav Peska Magdalena Piegza Robert Pudlo Rajkumar Ramamurthy Anita-Bella Réthi Timo Reuter Aleksandra Revina Michal Romaszewski Lars Schmidt-Thieme Piotr Scislo Przemyslaw Sekula Károly Simon Tomás Skopal Iga Stoklosa Mihai Suciu 0001 Annamária Szenkovits Nenad Tomasev Noémi Ágnes Varga Dániel T. Várkonyi Patrik Veselý Zoltán Vidnyánszky Stefan Wrobel