Publications

Publications of the IDA Lab since project start (March 2020)

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

[26]  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://arxiv.org/abs/2012.09737

[25] A. Kovacs-Györi, 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. ISPRS Int. J. Geo-Inf. (2020) https://doi.org/10.3390/ijgi9120752

[24] 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. Physical Review Accelerators and Beams (2020) https://doi.org/10.1103/PhysRevAccelBeams.23.124801

[23] T. Kasper, S. Fuchs, W. Trutschnig: On weak conditional convergence of bivariate Archimedean and Extreme Value copulas, and consequences to nonparametric estimation. to appear in Bernoulli (2020), preprint.pdf

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

[21] 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. to appear in Uniform Distribution Theory  (2020) https://content.sciendo.com/view/journals/udt/15/2/article-p99.xml?

[20] G. Zimmermann: To rank or to permute when comparing an ordinal outcome between two groups while adjusting for a covariate? In: La Rocca et al. (eds.), Nonparametric Statistics. 4th ISNPS, Salerno, Italy, Springer Proceedings in Mathematics and Statistics (2018) https://doi.org/10.1007/978-3-030-57306-5_48

[19] 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. Epilepsy Behav (2020) https://doi.org/10.1016/j.yebeh.2020.107460

[18] 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. Epilepsia (2020) https://doi.org/10.1111/epi.16737

[17] F.X.Vialard, R. Kwitt, S. Wei, M. Niethammer: A Shooting Formulation of Deep Learning. NeurIPS  2020 (ERA CORE A*) https://arxiv.org/abs/2006.10330

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

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

[14] S. Fuchs and K.D. Schmidt: On order statistics and Kendall’s tau for copulas. Statistics and Probability Letters (2021) https://doi.org/10.1016/j.spl.2020.108972

[13] J. Fernández Sánchez, J.B. Seoane-Sepúlveda, W. Trutschnig: Lineability, algebrability, and sequences of random variables. To appear in Mathematische Nachrichten (2020) preprint (pdf)

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

[11] M. Wagner, A.C. 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. Polar Biology (2020) https://doi.org/10.1007/s00300-020-02754-8

[10] A.S. Berghoff, M. Gansterer, A.C. 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. Journal of Clinical Onkology (2020) https://ascopubs.org/doi/10.1200/JCO.20.01442

[9] E. Hidalgo-Lopez, G. Zimmermann, B. Pletzer: Intra-subject consistency of spontaneous eye blink rate in young women across the menstrual cycle. Scientific Reports (2020) https://doi.org/10.1038/s41598-020-72749-2

[8] M. Happ, G. Zimmermann, E. Brunner, A. Bathke: Pseudo-Ranks: How to Calculate Them Efficiently. R. Journal of Statistical Software, CodeSnippets (2020) http://doi.org/10.18637/jss.v095.c01

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

[6] 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. To appear in Revista de la Real Academia de Ciencias Exactas, Físicas y Naturales. Serie A. Matemáticas (2020) preprint (pdf)

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

[4] 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.  Journal of Mathematical Analysis and Applications (2020) https://doi.org/10.1016/j.jmaa.2020.124433

[3] C. Hofer, F. Graf, B. Rieck, M. Niethammer, R. Kwitt: Graph Filtration Learning. ICML 2020 (CORE: A*), https://arxiv.org/abs/1905.10996

[2] C. Hofer, F. Graf, M. Niethammer, R. Kwitt: Topologically Densified Distributions. ICML 2020 (CORE: A*), https://arxiv.org/abs/2002.04805

[1] C. Borgelt, O. Yarikova: Initializing k-Means Clustering. International Conference on Data Science, Technology and Applications (DATA) 2020, http://www.dataconference.org/

 

 

 

Scroll to top