Publications

Plant C, Biedermann S, Böhm C. Data Compression as a Comprehensive Framework for Graph Drawing and Representation Learning. In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, CA, USA, August 23-27, 2020. ACM. 2020. p. 1212-1222


Altinigneli MC, Miklautz L, Böhm C, Plant C. Hierarchical Quick Shift Guided Recurrent Clustering. In 36th IEEE International Conference on Data Engineering, ICDE 2020, Dallas, TX, USA, April 20-24, 2020. IEEE. 2020. p. 1842-1845


Behzadi S, Schelling B, Plant C. ITGH: Information-Theoretic Granger Causal Inference on Heterogeneous Data. In Lauw H, Wong RW, Ntoulas A, Lim EP, Ng SK, Pan S, editors, Advances in Knowledge Discovery and Data Mining. PAKDD 2020. Cham: Springer. 2020. p. 742-755. (Lecture Notes in Computer Science, Vol. 12085).


Plant C, Böhm C. Massively Parallel Random Number Generation. In Wu XT, Jermaine C, Xiong L, Hu XH, Kotevska O, Lu SY, Xu WJ, Aluru S, Zhai CX, Al-Masri E, Chen ZY, Saltz J, editors, 2020 IEEE International Conference on Big Data: Dec 10-Dec 13, 2020, virtual event : proceedings. Piscataway, NJ: IEEE. 2020. p. 413-419 doi.org/10.1109/BigData50022.2020.9377814


Berner J, Dablander M, Grohs P. Numerically Solving Parametric Families of High-Dimensional Kolmogorov Partial Differential Equations via Deep Learning. In Larochelle H, Ranzato M, Hadsell R, Balcan MF, Lin H, editors, Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual. Cambridge, Mass.: MIT Press. 2020. (Advances in neural information processing systems : ... proceedings of the ... conference, Vol. 33). doi.orghttps://proceedings.neurips.cc/paper/2020/file/c1714160652ca6408774473810765950-Paper.pdf