IDA LAB

[152] S. Fuchs, C. Limbach, F. Schürrer: On exact regions between measures of concordance and Chatterjee’s rank correlation for lower semilinear copulas. (2025) https://doi.org/10.1016/j.ijar.2025.109588

[151] H. Kaiser: An asymptotic expansion for the Mellin transform of a beta function and applications. (2025) https://doi.org/10.1080/10652469.2025.2565257

[150] J. F. Sánchez, W. Trutschnig: On bivariate Archimedean copulas with fractal support. (2025) https://doi.org/10.1515/demo-2025-0013

[149] L. Maislinger, W. Trutschnig: On bivariate lower semilinear copulas and the star product. (2025) https://doi.org/10.1016/j.ijar.2025.109366

[148] F. Durante, S. Fuchs, R. Pappadà: Clustering of compound events based on multivariate comonotonicity. (2025) https://doi.org/10.1016/j.spasta.2025.100881

[147] M. Tschimpke, M. Schreyer, W. Trutschnig: Revisiting the region determined by Spearman’s ρ and Spearman’s footrule ϕ. (2025) https://doi.org/10.1016/j.cam.2024.116259

[146] J. Beck, P. Langthaler, A. Bathke: Combining stochastic tendency and distribution overlap towards improved nonparametric effect measures and inference. (2025) https://doi.org/10.1111/sjos.12783

[145] A. Kovács-Győri, D. Lahnsteiner, J. Schmitt, T. Prinz: Spatiotemporal clustering based on internationaltourists’ overnight stay data in Salzburg, Austria: aseasonal analysis using space-time data cubes toenhance airport connectivity. (2025) https://doi.org/10.1080/02508281.2024.2443728

[144] N. Dietrich, W. Trutschnig: On differentiability and mass distributions of typical bivariate copulas. (2025) https://doi.org/10.1016/j.fss.2024.109150

[143] M. Kiefel, J. Freidl: Nonparametric Analysis of Multivariate Data in Factorial Designs with Nondetects: A Case Study with Microbiome Data. (2024) https://doi.org/10.1007/s13253-024-00671-5

[142] F. Petersen, H. Kuehne, C. Borgelt, J. Welzel, S. Ermon: Convolutional Differentiable Logic Gate Networks. (2024) https://openreview.net/pdf?id=4bKEFyUHT4

[141] V. N. Frey, P. Langthaler, T. Prinz, et al.: Stress and the City: Mental Health in Urbanized vs. Rural Areas in Salzburg, Austria. (2024) https://doi.org/10.3390/ijerph21111459

[140] P. Langthaler, K. P. Gladow, O. Krüger, J. Beck: A Novel Method for Nonparametric Statistical Inference for Niche Overlap in Multiple Species. (2024) https://doi.org/10.1002/bimj.202400013

[139] B. Taxer, W. Lauth, M. Christova, et al.: Exploring Facial Somatosensory Distortion in Chronic Migraine: The Role of Laterality and Emotion Recognition—A Cross-Sectional Study. (2024) https://doi.org/10.3390/app14188102

[138] P. Langthaler, J. Ansari, S. Fuchs, W. Trutschnig: Constructing measures of dependence via sensitivity of conditional distributions. (2024) https://doi.org/10.1007/978-3-031-65993-5_28

[137] C. Limbach, S. Fuchs: Quantifying Directed Dependence with Kendall’s Tau. (2024) https://doi.org/10.1007/978-3-031-65993-5_30

[136] Y. Wang, S. Fuchs: Hierarchical Variable Clustering Based on Measures of Predictability. (2024) https://doi.org/10.1007/978-3-031-65993-5_67

[135] J. Ansari, S. Fuchs, W. Trutschnig, et al.: Combining, Modelling and Analyzing Imprecision, Randomness and Dependence. (2024) https://doi.org/10.1007/978-3-031-65993-5

[134] S. Hirländer, S. Pochaba, C. Xu, et al.: Deep Meta Reinforcement Learning for Rapid Adaptation In Linear Markov Decision Processes: Applications to CERN’s AWAKE Project. (2024) https://doi.org/10.1007/978-3-031-65993-5_21

[133] S. Pochaba, R. Kwitt, S. Hirländer, et al.: Multi-agent Reinforcement Learning and Its Application to Wireless Network Communication. (2024) https://doi.org/10.1007/978-3-031-65993-5_45

[132] G. Schäfer, S. Huber, S. Hirländer, et al.: Python-Based Reinforcement Learning on Simulink Models. (2024) https://doi.org/10.1007/978-3-031-65993-5_55

[131] H. Kaiser: Common Models of Errors in Variables. (2024) https://doi.org/10.1007/978-3-031-65993-5_25

[130] J. Ansari, E. Lütkebohmert, M. Rockel: An Empirical Study on New Model-Free Multi-output Variable Selection Methods. (2024) https://doi.org/10.1007/978-3-031-65993-5_2

[129] A. Romagna, M. Geroldinger, et al.: Wound healing after intracutaneous vs. staple-assisted skin closure in lumbar, non-instrumented spine surgery: a multicenter prospective randomized trial. (2024) https://doi.org/10.1007/s00701-024-06227-3

[128] A. Astner-Rohracher, G. Zimmermann, B. Frauscher, et al.: Prognostic value of the 5-SENSE Score to predict focality of the seizure-onset zone as assessed by stereoelectroencephalography: a prospective international multicentre validation study. (2024) https://doi.org/10.1136/bmjno-2024-000765

[127] A. E. Carrozzo, A. Bathke, G. Zimmermann, et al.: Applying Exercise Capacity and Physical Activity as Single vs Composite Endpoints for Trials of Cardiac Rehabilitation Interventions: Rationale, Use-case, and a Blueprint Method for Sample Size Calculation. (2024) https://doi.org/10.1016/j.apmr.2024.04.004

[126] G. Kalss, G. Zimmermann, et al.: The Fingerprint of Scalp-EEG in Drug-Resistant Frontal Lobe Epilepsies. (2024) https://doi.org/10.1097/WNP.0000000000001106

[125] J. Ansari, M. Rockel: Dependence properties of bivariate copula families. (2024) https://doi.org/10.1515/demo-2024-0002

[124] J. Ansari, E. Lütkebohmert, A. Neufeld, J. Sester: Improved robust price bounds for multiasset derivatives under market-implied dependence information. (2024) https://doi.org/10.1007/s00780-024-00539-z

[123] T. Pixner, W. Lauth, G. Zimmermann, et al.: Rise in fasting and dynamic glucagon levels in children and adolescents with obesity is moderate in subjects with impaired fasting glucose but accentuated in subjects with impaired glucose tolerance or type 2 diabetes. (2024) https://doi.org/10.3389/fendo.2024.1368570

[122] J. Nyberg, G. Zimmermann, M. Geroldinger, K. E. Thiel, et al.: Optimizing designs in clinical trials with an application in treatment of Epidermolysis bullosa simplex, a rare genetic skin disease. (2024) https://doi.org/10.1016/j.csda.2024.108015

[121] S. Hirländer, L. Lamminger, S. Pochaba, J. Kaiser, C. Xu, A. Santamaría, L. Scomparin, V. Kain: Towards few-shot reinforcement learning in particle accelerator control. (2024) https://doi.org/10.18429/JACoW-IPAC2024-TUPS59

[120] S. Hirländer, S. Appel, N. Madysa: Data-Driven model predictive control for automated optimitization of injection into the SIS18 synchrotron. (2024) https://doi.org/10.18429/JACoW-IPAC2024-TUPS59

[119] A. Santamaría, C. Xu, L. Scomparin, S. Hirländer, S. Pochaba, A. Eichler, J. Kaiser, M. Schenk: The Reinforcerment Learning for Autonomous Accelerators Collaboration. (2024) https://doi.org/10.18429/JACoW-IPAC2024-TUPS62

[118] A. Domnica Hoeggerl, W. Lauth, G. Zimmermann, et al.: Dissecting the dynamics of SARS-CoV-2 reinfections in blood donors with pauci- or asymptomatic COVID-19 disease course at initial infection. (2024) https://doi.org/10.1080/23744235.2024.2367112

[117] S. Fuchs: Quantifying directed dependence via dimension reduction. (2024) https://doi.org/10.1016/j.jmva.2023.105266

[116] K. Zeman-Kuhnert, G. Zimmermann, W. Lauth, et al.: Long-Term Outcomes of Dental Rehabilitation and Quality of Life after Microvascular Alveolar Ridge Reconstruction in Patients with Head and Neck Cancer. (2024) https://doi.org/10.3390/jcm13113110

[115] V. N. Frey,  P. Langthaler, G. Zimmermann, et al.: Influence of sports on cortical excitability in patients with spinal cord injury: a TMS study. (2024) https://doi.org/10.3389/fmedt.2024.1297552

[114] P. Sattler, G. Zimmermann: Choice of the hypothesis matrix for using the Wald-type-statistic. (2024) https://doi.org/10.1016/j.spl.2024.110038

[113] S. Fuchs, Y. Wang: Hierarchical variable clustering based on the predictive strength between random vectors. (2024) https://doi.org/10.1016/j.ijar.2024.109185

[112] T. Pollhammer, B. Salcher, S. Fuchs: MAMU: an R package for GIS-based river terrace mapping, morphostratigraphic evaluation of terrace maps and outcrop data and river long profile modelling. (2024) https://doi.org/10.5194/egusphere-egu24-16766

[111] S. Deininger, W. Lauth, et al.: Functional Outcome and Safety of Endoscopic Treatment Options for Benign Prostatic Obstruction (BPO) in Patients ≥ 75 Years of Age. (2024) https://doi.org/10.3390/jcm13061561

[110] J. Ansari, L. Rüschendorf: Supermodular and directionally convex comparison results for general factor models. (2024) https://doi.org/10.1016/j.jmva.2023.105264

[109] S. Schoenen, G. Zimmermann, et al.: Istore: a project on innovative statistical methodologies to improve rare diseases clinical trials in limited populations. (2024) https://doi.org/10.1186/s13023-024-03103-2

[108] S. Fuchs, M. Tschimpke: A novel positive dependence property and its impact on a popular class of concordance measures. (2024) https://doi.org/10.1016/j.jmva.2023.105259

[107] P. Bosque Varela, G. Zimmermann, E. Trinka, et al.: Magnetic resonance imaging fingerprints of status epilepticus: A case-control study. (2024) https://doi.org/10.1111/epi.17949

[106] J. Ansari, T. Shushi, S. Vanduffel: Up- and down-correlations in normal variance mixture models. (2024) https://doi.org/10.1016/j.spl.2023.109949

[105] R. Kozlica, G. Schäfer, S. Hirländer, S. Wegenkittl: A Modular Test Bed for Reinforcement Learning Incorporation into Industrial Applications. (2024) https://doi.org/10.1007/978-3-031-42171-6_15

[104] T. Kasper, N. Dietrich, W. Trutschnig: On convergence and mass distributions of multivariate Archimedean copulas and their interplay with the Williamson transform. (2024) https://doi.org/10.1016/j.jmaa.2023.127555

[103] J. Verbeeck, M. Geroldinger, K. Thiel, […], G. Zimmermann: How to analyze continuous and discrete repeated measures in small sample cross-over trials?. (2023) https://doi.org/10.1111/biom.13920

[102] M. Geroldinger, J. Verbeeck, A.C. Hooker, K.E. Thiel, G. Molenberghs, J. Nyberg, J. Bauer, M. Laimer, V. Wally, A. Bathke, G. Zimmermann: Statistical recommendations for count, binary, and ordinal data in rare disease cross-over trials. (2023) https://doi.org/10.1186/s13023-023-02990-1

[101] F. Berns, G. Zimmermann, C. Borgelt, et al.: Trustworthy Medical Operational AI: Marrying AI and Regulatory Requirements. (2023) https://doi.org/10.1109/BigData59044.2023.10386683

[100] J. Fernández Sánchez, W. Trutschnig: A link between Kendall’s τ , the length measure andthe surface of bivariate copulas, and a consequence to copulas with self-similar support. (2023) https://doi.org/10.1515/demo-2023-0105

[99] L. J. Rainer, E. Trinka, P. Langthaler, G. Zimmermann, L. Kronbichler, et al.: Recognition and Perception of emotions in juvenile myoclonic epilepsy. (2023) https://doi.org/10.1111/epi.17783

[98] S. Hirländer, L. Lamminger, G. Zevi-Della-Pora, V. Kain: Ultra fast reinforcement learning in accelerator control demonstrated on CERN AWAKE. (2023) https://doi.org/10.18429/jacow-ipac2023-thpl038

[97] A. Oeftiger, S. Garcia, J. Lagrange, S. Hirländer: Active Deep Learning for Nonlinear Optics Design of a Vertical FFA Accelerato. (2023) https://doi.org/10.18429/jacow-ipac2023-wepa026

[96] T. Moser, G. Zimmermann, et al.: Long-term outcome of natalizumab-associated progressive multifocal leukoencephalopathy in Austria: a nationwide retrospective study. (2023) https://doi.org/10.1007/s00415-023-11924-7

[95] R. Kozlica, S. Wegenkittl, S. Hirländer: Deep Q-Learning versus Proximal Policy Optimization: Performance Comparison in a Material Sorting Task. (2023) https://doi.org/10.1109/isie51358.2023.10228056

[94] M. Hanusch, X. He, S. Janssen, J. Selke, W. Trutschnig, R.R. Junker: Exploring the Frequency and Distribution of Ecological Non-monotonicity in Associations among Ecosystem Constituents. (2023) https://doi.org/10.1007/s10021-023-00867-9

[93] J. Fernández-Sánchez, J. López-Salazar Codes, J.B. Seoane Sepúlveda, W. Trutschnig: Generalized Notions of Continued Fractions: Ergodicity and Number Theoretic Applications (1st ed.). (2023) https://doi.org/10.1201/9781003404064

[92] O. Kartal, N. Lindlbauer, S. Laner-Plamberger, E. Rohde, F. Föttinger, L. Ombres, G. Zimmermann, C. Mrazek, W. Lauth, C. Grabmer: Collection efficiency of mononuclear cells in offline extracorporeal photopheresis: can processing time be shortened?. (2023) https://doi.org/10.2450/BloodTransfus.442

[91] Y. Deng, D. Lahnsteiner, T. Prinz: Promoting Active and Sustainable Commuting: A Tool for Analysing Location-specific Conditions and Potentials for Walking, Cycling and Public Transport. (2023) https://doi.org/10.1553/giscience2023_01_s101

[90] A.M. Wiesinger, B. Bigger, R. Giugliani, C. Lampe, M. Scarpa, T. Moser, C. Kampmann, G. Zimmermann, F.B. Lagler: An Innovative Tool for Evidence-Based, Personalized Treatment Trials in Mucopolysaccharidosis. (2023) https://doi.org/10.3390/pharmaceutics15051565

[89] S. Kranzinger, S. Baron, C. Borgelt, et al: Generalisability of Sleep Stage Classification Based on Interbeat Intervals: Validating Three Machine Learning Approaches on Self-recorded Test Data. (2023) https://doi.org/10.1007/s41237-023-00199-x

[88] F.M. Velotti, B. Goddard, V. Kain, R. Ramjiawan, G. Zevi Della Porta, S. Hirländer: Towards automatic setup of 18 MeV electron beamline using machine learning. (2023) https://doi.org/10.1088/2632-2153/acce21

[87] G. Schäfer, R. Kozlica, S. Wegenkittl, S.Huber: An Architecture for Deploying Reinforcement Learning in Industrial Environments. (2023) https://doi.org/10.1007/978-3-031-25312-6_67

[86] M. Geroldinger, J. Verbeeck, K.E. Thiel, G. Molenberghs, A. Bathke, M. Laimer, G. Zimmermann: A neutral comparison of statistical methods for analyzing longitudinally measured ordinal outcomes in rare diseases. (2023) https://doi.org/10.1002/bimj.202200236

[85] E. Trinka, L.J. Rainer, C.A. Granbichler, G. Zimmermann, M. Leitinger: Mortality, and life expectancy in Epilepsy and Status epilepticus — current trends and future aspects. (2023) https://doi.org/10.3389/fepid.2023.1081757

[84] F. Schöpflin, S. Erber, D. Madlener, T. Prinz: Densification of Single and Two-Family Houses considering Green Space and Mobility. (2022) https://doi.org/10.14311/APP.2022.38.0613

[83] W. Senker, H. Stefanits, S. Aspalter, W. Trutschnig, J. Franke, A. Gruber: Nonsteroidal anti-inflammatory drugs (NSAID) do not increase blood loss or the incidence of postoperative epidural hematomas when using minimally invasive fusion techniques in the degenerative lumbar spine. (2022) https://doi.org/10.3389%2Ffsurg.2022.1000238

[82] M. Genitrini, J. Fritz, G. Zimmermann, H. Schwameder: Downhill Sections Are Crucial for Performance in Trail Running Ultramarathons-A Pacing Strategy Analysis. (2022) https://doi.org/10.3390/jfmk7040103

[81] L. Grech, G. Valentino, D. Alves, S. Hirländer: Application of reinforcement learning in the LHC tune feedback. (2022) https://doi.org/10.3389/fphy.2022.929064

[80] F.M. Velotti, B. Goddard, V. Kain, R. Ramjiawan, G.Z.D. Porta, S. Hirländer: Automatic setup of 18 MeV electron beamline using machine learning. (2022) https://doi.org/10.48550/arXiv.2209.03183

[79] Á.K. Csete, A. Kovács-Győri, P. Szilassi: Age-group-based evaluation of residents’ urban green space provision: Szeged, Hungary. A case study. (2022) https://doi.org/10.15201/hungeobull.71.3.3

[78] M. Pallauf, F. Steinkohl, G. Zimmermann, et al.: External validation of two mpMRI-risk calculators predicting risk of prostate cancer before biopsy. (2022) https://doi.org/10.1007/s00345-022-04119-8

[77] S. Fuchs, M. Tschimpke: On positive dependence properties for Archimedean copulas. (2022) https://doi.org/10.1007/978-3-031-15509-3_21

[76] S. Fuchs: The simplifying assumption in pair-copula constructions from an analytic perspective. (2022) https://doi.org/10.1007/978-3-031-15509-3_20

[75] F. Griessenberger, W. Trutschnig: Maximal asymmetry of bivariate copulas and consequences to measures of dependence. (2022) https://doi.org/10.1515/demo-2022-0115

[74] F. Griessenberger, W. Trutschnig: qad: An R-package to detect asymmetric and directed dependence in bivariate samples. (2022) https://doi.org/10.1111/2041-210X.13951

[73] W. Trutschnig, F. Griessenberger: On Quantifying and Estimating Directed Dependence. (2022) https://doi.org/10.1007/978-3-031-15509-3_50

[72] N. Dietrich, J. Fernández Sánchez, W. Trutschnig: Convergence of Copulas Revisited: Different Notions of Convergence and Their Interrelations. (2022) https://doi.org/10.1007/978-3-031-15509-3_16

[71] T. Kasper, W. Trutschnig: A Markov Kernel Approach to Multivariate Archimedean Copulas. (2022) https://doi.org/10.1007/978-3-031-15509-3_30

[70] S. Laner-Plamberger S, […], W. Lauth, G. Zimmermann, et al.: SARS-CoV-2 IgG Levels Allow Predicting the Optimal Time Span of Convalescent Plasma Donor Suitability. (2022) https://doi.org/10.3390/diagnostics12112567

[69] S. Fuchs, M. Tschimpke: Total positivity of copulas from a Markov kernel perspective. (2022) https://doi.org/10.1016/j.jmaa.2022.126629

[68] L. Machegger […], T. Prüwasser, G. Zimmermann et al.: Quantitative Analysis of Diffusion-Restricted Lesions in a Differential Diagnosis of Status Epilepticus and Acute Ischemic Stroke. (2022) https://doi.org/10.3389/fneur.2022.926381

[67] T. Mroz, J. Fernández Sánchez, S. Fuchs, W. Trutschnig: On distributions with fixed marginals maximizing the joint or the prior default probability, estimation, and related results. (2022) https://doi.org/10.1016/j.jspi.2022.07.005

[66] C. Ferner, S. Wegenkittl: Benefits from Variational Regularization in Language Models. (2022) https://doi.org/10.3390/make4020025

[65] M. Happ, J.L. Du, M. Herlich, C. Maier, P. Dorfinger, J. Suárez-Varela: Exploring the Limitations of Current Graph Neural Networks for Network Modeling. (2022) https://doi.org/10.1109/NOMS54207.2022.9789708

[64] L.J. Rainer, M. Kronbichler, G. Kuchukhidze, E. Trinka, P. Langthaler, L. Kronbichler, S. Said-Yuerekli, M. Kirschner, G. Zimmermann, J. Höfler, E. Schmid, M. Braun: Emotional Word Processing in Patients With Juvenile Myoclonic Epilepsy. (2022) https://doi.org/10.3389/fneur.2022.875950

[63] M.J. Mair, J.M. Berger, M. Mitterer, P. Gattinger, J.M. Berger, W. Trutschnig, A. Bathke, et al.: Enhanced SARS-CoV-2 breakthrough infections in patients with hematologic and solid cancers due to Omicron. (2022) https://doi.org/10.1016/j.ccell.2022.04.003

[62] J. Carmona Tapia, J. Fernández Sánchez, J.B. Seoane-Sepúlveda, W. Trutschnig: Lineability, Spaceability, and Latticeability of subsets of C([0,1]) and Sobolev Spaces. (2022) https://doi.org/10.1007/s13398-022-01256-y

[61] F. Petersen, C. Borgelt, H. Kuehne, O. Deussen: GenDR: A Generalized Differentiable Renderer. (2022) https://doi.org/10.48550/arXiv.2204.13845

[60] J. Fernández-Sánchez, J.B. Seoane-Sepúlveda, W. Trutschnig: Lineability, algebrability, and sequences of random variables. (2022) https://doi.org/10.1002/mana.202000102

[59] K. Aleksovska, T. Kobulashvili, J. Costa, G. Zimmermann, et al.: European Academy of Neurology guidance for developing and reporting clinical practice guidelines on rare neurological diseases. (2022) https://doi.org/10.1111/ene.15267

[58] F. Petersen, C. Borgelt, H. Kuehne, O. Deussen: Monotonic Differentiable Sorting Networks. (2022) https://doi.org/10.48550/arXiv.2203.09630

[57] V. Nunhofer, L. Weidner, A.D. Hoeggerl, G. Zimmermann, et al.: Persistence of Naturally Acquired and Functional SARS-CoV-2 Antibodies in Blood Donors One Year after Infection. (2022) https://doi.org/10.3390/v14030637

[56] F. Griessenberger, R.R. Junker, W. Trutschnig: On a multivariate copula-based dependence measure and its estimation. (2022) https://doi.org/10.1214/22-EJS2005

[55] M.J. Mair, J.M. Berger, M. Mitterer, M. Gansterer, A. Bathke, W. Trutschnig, A. S. Berghoff, T. Perkmann, H. Haslacher, W.W. Lamm, M. Raderer, S. Tobudic, T. Fuereder, T. Buratti, D. Fong, M. Preusser: Third dose of SARS-CoV-2 vaccination in hemato-oncological patients and health care workers: immune responses and adverse events – a retrospective cohort study. (2022) https://doi.org/10.1016/j.ejca.2022.01.019

[54] S. Roesch, A. O’Sullivan, G. Zimmermann, A. Mair, C. Lipuš, J.A. Mayr, S.B. Wortmann and G. Rasp: Mitochondrial Disease and Hearing Loss in Children: A Systematic Review. (2022) https://doi.org/10.1002/lary.30067

[53] C. Ferner: Captioning Bosch: Captioning Bosch: A Twitter Bot. (2022) https://doi.org/10.24963/ijcai.2022/694

[52] F. Griessenberger, J. Fernández Sánchez, W. Trutschnig: Some properties of double shuffles of bivariate copulas and (extreme) copulas invariant with respect to Lüroth double shuffles. (2022) https://doi.org/10.1016/j.fss.2021.02.014

[51] M. Wagner, G. Brunauer, A. Bathke, S.C. Cary, R. Fuchs, L.G. Sancho, R. Türk, U. Ruprecht: Macroclimatic conditions as main drivers for symbiotic association patterns in lecideoid lichens along the Transantarctic Mountains, Ross Sea region, Antarctica. (2021) https://doi.org/10.1038/s41598-021-02940-6

[50] A. Astner-Rohracher, G. Zimmermann, T. Avigdor, et al.: Development and Validation of the 5-SENSE Score to Predict Focality of the Seizure-Onset Zone as Assessed by Stereoelectroencephalography. (2021) https://doi.org/10.1001/jamaneurol.2021.4405

[49] V. Kain, N. Bruchon, S. Hirländer, N. Madysa, I. Vojskovic, P.K. Skowronski, G. Valentino: Test of Machine Learning at the Cern LINAC4. (2021) https://doi.org/10.18429/JACoW-HB2021-TUEC4

[48] T. Kasper, S. Fuchs, W. Trutschnig: On weak conditional convergence of bivariate Archimedean and Extreme Value copulas, and consequences to nonparametric estimation. (2021) https://doi.org/10.3150/20-BEJ1306

[47] A. Egger-Rainer, S.M. Hettegger, R. Feldner, S. Arnold, C. Bosselmann, H. Hamer, A. Hengsberger, J. Lang, S. Lorenzl, H. Lerche, S. Noachtar, E. Pataraia, A. Schulze-Bonhage, A.M. Staack, E. Trinka, I. Unterberger, G. Zimmermann: Do all patients in the epilepsy monitoring unit experience the same level of comfort? A quantitative exploratory secondary analysis. (2021) https://doi.org/10.1111/jan.15105

[46] F. Petersen, C. Borgelt, H. Kuehne, O. Deussen: Learning with Algorithmic Supervision via Continuous Relaxation. (2021) https://doi.org/10.48550/arXiv.2110.05651

[45] G. Zimmermann, E. Brunner, W. Brannath, M. Happ, A. Bathke: Pseudo Ranks: The Better Way of Ranking?. (2021) https://doi.org/10.1080/00031305.2021.1972836

[44] T. Kasper, S. Fuchs, W. Trutschnig: On convergence of associative copulas and related results. (2021) https://doi.org/10.1515/demo-2021-0114

[43] F. Kröger, G. Weber, S. Hirländer, R. Alemany–Fernández, M. W. Krasny, T. Stohlker, I. Tolstikhina, V. Shevelko: Charge-state distributions of highly charged lead ions at relativistic collision energies. (2021) https://doi.org/10.1002/andp.202100245

[42] E. Gfrerer, D. Laina, G. Danae, M. Gibernau, R. Fuchs, M. Happ, T. Tolasch, W. Trutschnig, A.C. Hörger, H.P. Comes, S. Dötterl: Floral scents of a deceptive plant are hyperdiverse and under population-specific phenotypic selection. (2021) https://doi.org/10.3389/fpls.2021.719092

[41] T. Kovács, A. Kovács-Győri, B. Resch: #AllforJan: How Twitter Users in Europe Reacted to the Murder of Ján Kuciak—Revealing Spatiotemporal Patterns through Sentiment Analysis and Topic Modeling. (2021) https://www.mdpi.com/2220-9964/10/9/585#

[40] J.M. Berger, M. Gansterer, W. Trutschnig, A. Bathke, et al.: SARS-CoV-2 screening in cancer outpatients during the second wave of the COVID-19 pandemic. (2021) https://doi.org/10.1007/s00508-021-01927-7

[39] J. Suárez-Varela, M. Ferriol-Galmés, A. López, P. Almasan, G. Bernárdez, D. Pujol-Perich, K. Rusek, L. Bonniot, C. Neumann, F. Schnitzler, F. Taïani, M. Happ, C. Maier, J. Lei Du, M. Herlich, P. Dorfinger, N.V. Hainke, S. Venz, J. Wegener, H. Wissing, B. Wu, S. Xiao, P. Barlet-Ros, A. Cabellos-Aparicio: The Graph Neural Networking Challenge: A Worldwide Competition for Education in AI/ML for Networks. (2021) https://doi.org/10.1145/3477482.3477485

[38] M. Happ, M Herrlich, C. Maier, J. L. Du, P. Dorfinger: Graph‑neural‑network‑based delay estimation for communication networkswith heterogeneous scheduling policies. (2021) https://doi.org/10.52953/TEJX5530

[37] L. Weidner, V. Nunhofer, C. Jungbauer, A.D. Hoeggerl, L. Grüner, C. Grabmer, G. Zimmermann, E. Rohde, S. Laner-Plamberger: Seroprevalence of anti-SARS-CoV-2 total antibody is higher in younger Austrian blood donors. (2021) https://doi.org/10.1007/s15010-021-01639-0

[36] F. Konietschke, C. Cao, A. Gunawardana, G. Zimmermann: Analysis of covariance under variance heteroscedasticity in general factorial designs. (2021) https://doi.org/10.1002/sim.9092

[35] N. Bruchon, G. Fenu, G. Gaio, S. Hirländer, M. Lonza, F.A. Pellegrino, E. Salvato: An Online Iterative Linear Quadratic Approach for a Satisfactory Working Point Attainment at FERMI. (2021) https://doi.org/10.3390/info12070262

[34] F. Petersen, C. Borgelt, H. Kuehne, O. Deussen: Differentiable Sorting Networks for Scalable Sorting and Ranking Supervision. (2021) https://doi.org/10.48550/arXiv.2105.04019

[33] A. Bathke, M. Happ, M. Hummer: Indirekte Impfeffekte für Kinder und Jugendliche bei Durchimpfung der Erwachsenen. (2021) Executive Policy Brief

[32] J. Pilz, L. Hehenwarter, G. Zimmermann, G. Rendl, G. Schweighofer-Zwink, M. Beheshti, C. Pirich: Feasibility of equivalent performance of 3D TOF [18F]-FDG PET/CT with reduced acquisition time using clinical and semiquantitative parameters. (2021) https://doi.org/10.1186/s13550-021-00784-9

[31] A. Schenk, M. Neuhäuser, G.D. Ruxton, A. Bathke: Predictors of pre-European deforestation on Pacific islands: A re-analysis using modern multivariate non-parametric statistical methods. (2021) https://doi.org/10.1016/j.foreco.2021.119238

[30] V. Racher, C. Borgelt: Gradient Ascent for Best Response Regression. (2021) https://doi.org/10.1007/978-3-030-74251-5_12

[29] T. Mroz, S. Fuchs, W. Trutschnig: How simplifying and flexible is the simplifying assumption in pair-copula constructions – analytic answers in dimension three and a glimpse beyond. (2021) https://doi.org/10.1214/21-EJS1832

[28] J. Fernández Sánchez, W. Trutschnig, M. Tschimpke: Markov product invariance in classes of bivariate copulas characterized by univariate functions. (2021) https://doi.org/10.1016/j.jmaa.2021.125184

[27] S. Fuchs, F.M.L. Di Lascio and F. Durante: Dissimilarity functions for rank-based hierarchical clustering of continuous variables. (2021) https://doi.org/10.48550/arXiv.2007.04799

[26] F. Graf, C.D. Hofer, M. Niethammer, R. Kwitt: Dissecting Supervised Constrastive Learning. (2021) https://doi.org/10.48550/arXiv.2102.08817

[25] A. Kovács-Győri, B. Resch: Towards an automated spatial workflow for the global monitoring of public urban green accessibility in the light of the sustainable development goals. (2021) https://gispoint.de/index.php?eID=dumpFile&t=f&f=13767&token=3c9cbd4271a31ac59883f4e7c0f34fa4e3b80f5e&download=

[24] R.R. Junker, F. Griessenberger, W. Trutschnig: Estimating scale-invariant directed dependence of bivariate distributions. (2021) https://doi.org/10.1016/j.csda.2020.107058

[23] W. Senker, H. Stefanits, M. Gmeiner, W. Trutschnig, C. Radl, A. Gruber: The Influence of Smoking in Minimally Invasive Spinal Fusion Surgery. (2021) https://doi.org/10.1515/med-2021-0223

[22] S. Fuchs, W. Trutschnig: On quantile-based co-risk measures and their estimation. (2020) https://doi.org/10.1515/demo-2020-0021

[21] V. Kain, S. Hirländer, B. Goddard, F.M.Velotti, G. Zevi Della Porta, N. Bruchon, G. Valentino: Sample-efficient reinforcement learning for CERN accelerator control. (2020) https://doi.org/10.1103/PhysRevAccelBeams.23.124801

[20] A. Kovács-Győri, A. Ristea, C. Havas, M. Mehaffy, H.H. Hochmair, B. Resch, L. Juhasz, A. Lehner, L. Ramasubramanian, T. Blaschke: Opportunities and Challenges of Geospatial Analysis for Promoting Urban Livability in the Era of Big Data and Machine Learning. (2020) https://doi.org/10.3390/ijgi9120752

[19] F. Durante, J. Fernández Sánchez, W. Trutschnig, M. Úbeda-Flores: On the size of subclasses of quasi-copulas and their Dedekind-MacNeille completion. (2020) https://doi.org/10.3390/math8122238

[18] T. Kiesslich, M. Beyreis, G. Zimmermann, A. Traweger: Citation inequality and the Journal Impact Factor: median, mean, (does it) matter? . (2020) https://doi.org/10.1007/s11192-020-03812-y

[17] G. Zimmermann: To rank or to permute when comparing an ordinal outcome between two groups while adjusting for a covariate?. (2020) https://doi.org/10.1007/978-3-030-57306-5_48

[16] M. Leitinger, K.N. Poppert, M. Mauritz, F. Rossini, G. Zimmermann, A. Rohracher, G. Kalss, G. Kuchukhidze, J. Höfler, P. Bosque Varela, R. Kreidenhuber, K. Volna, C. Neuray, T. Kobulashvili, C.A. Granbichler, U. Siebert, E. Trinka: Status epilepticus admissions during the COVID-19 pandemic in Salzburg. A population-based study. (2020) https://doi.org/10.1111/epi.16737

[15] J. Fernández Sánchez, D.L. Rodríguez-Vidanes, J.B. Seoane-Sepúlveda, W. Trutschnig: Lineability, differentiable functions and special derivatives. (2020) https://doi.org/10.1007/s43037-020-00103-9

[14] J.Y. Ahn, S. Fuchs, R. Oh: A copula transformation in multivariate mixed discrete-continuous models. (2020) https://doi.org/10.1016/j.fss.2020.11.008

[13] F. Durante, J. Fernández Sánchez, C. Ignazzi, W. Trutschnig: On extremal problems for pairs of uniformly distributed sequences and integrals with respect to copula measures. (2020) https://doi.org/10.2478/udt-2020-0013

[12] M. Wagner, A. Bathke, S.C. Cary, T.G.A. Green, R.R. Junker, W. Trutschnig, U. Ruprecht: Myco- and photobiont associations in crustose lichens in the McMurdo Dry Valleys (Antarctica) reveal high differentiation along an elevational gradient. (2020) https://doi.org/10.1007/s00300-020-02754-8

[11] S. Fuchs, K.D. Schmidt: On order statistics and Kendall’s tau for copulas. (2020) https://doi.org/10.1016/j.spl.2020.108972

[10] E. Brunner, F. Konietschke, A. Bathke, M. Pauly: Ranks and Pseudo-ranks—Surprising Results of Certain Rank Tests in Unbalanced Designs. (2020) https://doi.org/10.1111/insr.12418

[9] L. Bernal-González, J. Fernández Sánchez, J.B. Seoane-Sepúlveda, W. Trutschnig: Highly tempering infinite matrices II: From divergence to convergence via Toeplitz-Silverman matrices. (2020) https://doi.org/10.1007/s13398-020-00934-z

[8] G. Zimmermann, E. Trinka: Accounting for individual variability in baseline seizure frequencies when planning randomized clinical trials remains challenging. (2020) https://doi.org/10.1111/epi.16676

[7] A. Egger-Rainer, E. Trinka, G. Zimmermann, S. Arnold, C. Boßelmann, H. Hamer, A. Hengsberger, J. Lang, H. Lerche, S. Noachtar, E. Pataraia, A. Schulze-Bonhage, A.M. Staack, I. Unterberger, S. Lorenzl: Assessing comfort in the epilepsy monitoring unit: Psychometric testing of an instrument. (2020) https://doi.org/10.1016/j.yebeh.2020.107460

[6] M.R. Berthold, C. Borgelt, F. Höppner, F. Klawonn, R. Silipo: Guide to Intelligent Data Science (2nd edition). (2020) https://doi.org/10.1007/978-3-030-45574-3

[5] J. Fernández-Sánchez, D.L. Rodríguez-Vidanes, J.B. Seoane-Sepúlveda, W. Trutschnig: Lineability and integrability in the sense of Riemann, Lebesgue, Denjoy, and Khintchine. (2020) https://doi.org/10.1016/j.jmaa.2020.124433

[4] A.S. Berghoff, M. Gansterer, A. Bathke, W. Trutschnig, P. Hungerländer, J.M.Berger, J. Kreminger, A.M. Starzer, R. Strassl, R. Schmid, H. Willschke, W. Lamm, M. Raderer, A.D. Gottlieb, N. J. Mauser, M. Preusser: SARS-CoV-2 Testing in Patients With Cancer Treated at a Tertiary Care Hospital During the COVID-19 Pandemic. (2020) https://ascopubs.org/doi/10.1200/JCO.20.01442

[3] F.X.Vialard, R. Kwitt, S. Wei, M. Niethammer: A Shooting Formulation of Deep Learning. (2020) https://doi.org/10.48550/arXiv.2006.10330

[2] S. Hirländer, N. Bruchon: Model-free and Bayesian Ensembling Model-based Deep Reinforcement Learning for Particle Accelerator Control Demonstrated on the FERMI FEL. (2020) https://doi.org/10.48550/arXiv.2006.10330

[1] C. Borgelt: Even Faster Exact k-Means Clustering. (2020) https://doi.org/10.1007/978-3-030-44584-3_8