Researcher Portfolio
Marx, Alexander
Databases and Information Systems, MPI for Informatics, Max Planck Society
Researcher Profile
Position: Databases and Information Systems, MPI for Informatics, Max Planck Society
Researcher ID: https://pure.mpg.de/cone/persons/resource/persons206670
Publications
: Marx, A., & Fischer, J. (2022). Estimating Mutual Information via Geodesic kNN. In Proceedings of the SIAM International Conference on Data Mining (pp. 415-423). Philadelphia, PA: SIAM. doi:10.1137/1.9781611977172.47. [PubMan] : Marx, A., Yang, L., & van Leeuwen, M. (2021). Estimating Conditional Mutual Information for Discrete-Continuous Mixtures using Multidimensional Adaptive Histograms. In Proceedings of the SIAM International Conference on Data Mining (pp. 387-395). Philadelphis, PA: SIAM. doi:10.1137/1.9781611976700.44. [PubMan] : Marx, A., & Fischer, J. (2021). Estimating Mutual Information via Geodesic kNN. Retrieved from https://arxiv.org/abs/2110.13883. [PubMan] : Marx, A., Gretton, A., & Mooij, J. M. (2021). A Weaker Faithfulness Assumption based on Triple Interactions. Retrieved from https://arxiv.org/abs/2010.14265. [PubMan] : Mian, O. A., Marx, A., & Vreeken, J. (2021). Discovering Fully Oriented Causal Networks. In Thirty-Fifth AAAI Conference on Artificial Intelligence (pp. 8975-8982). Palo Alto, CA: AAAI. doi:10.1609/aaai.v35i10.17085. [PubMan] : Schmidt, F., Marx, A., Baumgarten, N., Hebel, M., Wegner, M., Kaulich, M., Leisegang, M. S., Brandes, R. P., Göke, J., Vreeken, J., & Schulz, M. H. (2021). Integrative Analysis of Epigenetics Data Identifies Gene-specific Regulatory Elements. Nucleic Acids Research (London), 49(18), 10397-10418. doi:10.1093/nar/gkab798. [PubMan] : Marx, A. (2021). Information-Theoretic Causal Discovery. PhD Thesis, Universität des Saarlandes, Saarbrücken. doi:10.22028/D291-34290. [PubMan] : Marx, A., & Vreeken, J. (in press). Approximating Algorithmic Conditional Independence for Discrete Data. In Proceedings of the First AAAI Spring Symposium Beyond Curve Fitting: Causation, Counterfactuals, and Imagination-based AI. [PubMan] : Marx, A., & Vreeken, J. (2019). Telling Cause from Effect by Local and Global Regression. Knowledge and Information Systems, 60(3), 1277-1305. doi:10.1007/s10115-018-1286-7. [PubMan] : Marx, A., & Vreeken, J. (2019). Causal Inference on Multivariate and Mixed-Type Data. In M. Berlingerio, F. Bonchi, T. Gärtner, N. Hurley, & G. Ifrim (Eds. ), Machine Learning and Knowledge Discovery in Databases (pp. 655-671). Berlin: Springer. doi:10.1007/978-3-030-10928-8_39. [PubMan] : Marx, A., & Vreeken, J. (2019). Testing Conditional Independence on Discrete Data using Stochastic Complexity. Retrieved from http://arxiv.org/abs/1903.04829. [PubMan] : Marx, A., & Vreeken, J. (2019). Testing Conditional Independence on Discrete Data using Stochastic Complexity. In K. Chaudhuri, & M. Sugiyama (Eds. ), Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (pp. 496-505). PMLR. [PubMan] : Marx, A., & Vreeken, J. (2019). Identifiability of Cause and Effect using Regularized Regression. In KDD '19 (pp. 852-861). New York, NY: ACM. doi:10.1145/3292500.3330854. [PubMan] : Marx, A., & Vreeken, J. (2018). Causal Discovery by Telling Apart Parents and Children. Retrieved from http://arxiv.org/abs/1808.06356. [PubMan] : Marx, A., & Vreeken, J. (2018). Stochastic Complexity for Testing Conditional Independence on Discrete Data. In Proceedings of the NeurIPS 2018 workshop on Causal Learning. Retrieved from https://drive.google.com/file/d/1mMkO5YZ5gkBRRFbfYb4DDRCsCN243eb2/view. [PubMan] : Marx, A., & Vreeken, J. (2017). Causal Inference on Multivariate Mixed-Type Data by Minimum Description Length. Retrieved from http://arxiv.org/abs/1702.06385. [PubMan] : Marx, A., & Vreeken, J. (2017). Telling Cause from Effect using MDL-based Local and Global Regression. doi:10.1109/ICDM.2017.40. [PubMan] : Marx, A., & Vreeken, J. (2017). Telling Cause from Effect Using MDL-Based Local and Global Regression. In 17th IEEE International Conference on Data Mining (pp. 307-316). Piscataway, NJ: IEEE. doi:10.1109/ICDM.2017.40. [PubMan] : Marx, A., Backes, C., Meese, E., Lenhof, H.-P., & Keller, A. (2016). EDISON-WMW: Exact Dynamic Programing Solution of the Wilcoxon–Mann–Whitney Test. Genomics, Proteomics & Bioinformatics, 14(1), 55-61. doi:10.1016/j.gpb.2015.11.004. [PubMan]