Publications

Bettiol E, Bomze I, Létocart L, Rinaldi F, Traversi E. Mining for diamonds: Matrix generation algorithms for binary quadratically constrained quadratic problems. Computers and Operations Research. 2022 Jun;142:105735. doi: 10.1016/j.cor.2022.105735

Kriege NM. Weisfeiler and Leman Go Walking: Random Walk Kernels Revisited. arXiv.org. 2022 May 22. doi: https://doi.org/10.48550/arXiv.2205.10914

Scherbela M, Reisenhofer R, Gerard L, Marquetand P, Grohs P. Solving the electronic Schrödinger equation for multiple nuclear geometries with weight-sharing deep neural networks. Nature Computational Science. 2022 May 19;2(5):331–341. doi: 10.1038/s43588-022-00228-x

Bomze I , Gabl M, Maggioni F, Pflug G. Two-stage stochastic standard quadratic optimization. European Journal of Operational Research. 2022 May 16;299(1):21-34. doi: 10.1016/j.ejor.2021.10.056

Molnar C, König G, Herbinger J, Freiesleben T, Dandl S, Scholbeck CA et al. General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models. In Holzinger A, Goebel R, Fong R, Moon T, Müller K-R, Samek W, editors, xxAI - Beyond Explainable AI: International Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Papers. 1 ed. Cham: Springer. 2022. p. 39-68. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Epub 2021 Aug 17. doi: https://doi.org/10.1007/978-3-031-04083-2_4

Oettershagen L, Mutzel P, Kriege NM. Temporal Walk Centrality: Ranking Nodes in Evolving Networks. In Laforest F, Troncy R, editors, WWW 2022 - Proceedings of the ACM Web Conference 2022. New York, NY: Association for Computing Machinery (ACM). 2022. p. 1640-1650 doi: https://doi.org/10.48550/arXiv.2202.03706, 10.1145/3485447.3512210

Bomze I , Gabl M. Uncertainty preferences in robust mixed-integer linear optimization with endogenous uncertainty. SIAM Journal on Optimization. 2022 Mar 24;32(1):292-318. doi: 10.1137/20M1355422

Bomze I, Rinaldi F, Zeffiro D. Fast cluster detection in networks by first-order optimization. SIAM Journal on Mathematics of Data Science. 2022 Mar 4;4(1):285-305. Epub 2021 Mar 29. doi: 10.1137/21M1408658

Bauer L, Hirsch F, Jones C, Hollander M, Grohs P, Anand A et al. Quantification of Kuramoto Coupling Between Intrinsic Brain Networks Applied to fMRI Data in Major Depressive Disorder. Frontiers in Computational Neuroscience. 2022 Mar 3;16:729556. doi: 10.3389/fncom.2022.729556

Schneckenreiter G, Herrmann L, Reisenhofer R, Popper N, Grohs P. Assessing the heterogeneity in the transmission of infectious diseases from time series of epidemiological data. medRxiv. 2022 Mar 1. doi: https://doi.org/10.1101/2022.02.21.22271241

Kinast A , Dörner KF, Rinderle-Ma S. Combing metaheuristics and process mining: Improving cobot placement in a combined cobot assignment and job shop scheduling problem. In Procedia Computer Science. Vol. 200. Elsevier. 2022. p. 1836-1845 doi: 10.1016/j.procs.2022.01.384

Huggins JE, Krusienski D, Vansteensel MJ, Valeriani D, Thelen A, Stavisky S et al. Workshops of the eighth international brain–computer interface meeting: BCIs: the next frontier. Brain-Computer Interfaces. 2022 Feb 8;9(2):69-101. doi: 10.1080/2326263X.2021.2009654

Kutyniok G, Petersen PC, Raslan M, Schneider R. A theoretical analysis of deep neural networks and parametric PDEs. Constructive Approximation. 2022 Feb;55(1):73-125. Epub 2021 Jun 2. doi: 10.1007/s00365-021-09551-4

Elbrächter D, Grohs P, Jentzen A, Schwab C. DNN Expression Rate Analysis of High-dimensional PDEs: Application to Option Pricing. Constructive Approximation. 2022 Feb;55(1):3-71. Epub 2021 May 6. doi: 10.1007/s00365-021-09541-6

Braune R, Benda F, Dörner KF , Hartl R. A Genetic Programming Learning Approach to Generate Dispatching Rules for Flexible Shop Scheduling Problems. International Journal of Production Economics. 2022 Jan;243:108342. doi: 10.1016/j.ijpe.2021.108342

Markham A, Das R, Grosse-Wentrup M. A Distance Covariance-based Kernel for Nonlinear Causal Clustering in Heterogeneous Populations. Proceedings of Machine Learning Research (PMLR). 2022;177:542-558.

Leiber C, Mautz D, Plant C , Böhm C. Automatic Parameter Selection for Non-Redundant Clustering. In Banerjee A, Zhou Z-H, Papalexakis EE, Riondato M, editors, Proceedings of the 2022 SIAM International Conference on Data Mining, SDM 2022, Alexandria, VA, USA, April 28-30, 2022. SIAM. 2022. p. 226-234 doi: 10.1137/1.9781611977172.26

Durani W, Mautz D, Plant C , Böhm C. DBHD: Density-based clustering for highly varying density. In IEEE International Conference on Data Mining, ICDM 2022, Orlando, FL, USA, November 28 - Dec. 1, 2022. IEEE. 2022. p. 921-926

Velaj Y , Dolezal D, Ambros R, Plant C , Motschnig R. Designing a Data Science Course for Non-Computer Science Students: Practical Considerations and Findings. In 2022 IEEE Frontiers in Education Conference, FIE 2022. Piscataway, NJ: IEEE. 2022. p. 1-9 doi: 10.1109/FIE56618.2022.9962455

Wu H, Tan S, Li W, Garrard M, Obeng A, Dimmery D et al.. Distilling Heterogeneity: From Explanations of Heterogeneous Treatment Effect Models to Interpretable Policies. 2022. Paper presented at 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Washington DC, District of Columbia, United States.