Univ.Ass. Dipl.-Ing. Dr.techn.Marcus MayrhoferBSc
Assistant, Research Unit of Computational Statistics
Phone: +43 1 58801 105687 Call Marcus Mayrhofer
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I am a PostDoc researcher at the Computational Statistics (CSTAT) research unit at TU Wien. I did my PhD on Robustness and Explainable Outlier Detection for Multivariate, Matrix-variate, and Functional Settings, opens an external URL in a new window, under the supervision of Univ.-Prof. Dipl.-Ing. Dr.techn. Peter Filzmoser.
Links: Researchgate, opens an external URL in a new window and ORCID, opens an external URL in a new window
Publications & Conferences
- | Expainable outlier detection for multivariate random processes with separable covariance structure at reposiTUm , opens an external URL in a new windowRadojicic, U., Mayrhofer, M., & Filzmoser, P. (2024, December 17). Expainable outlier detection for multivariate random processes with separable covariance structure. ICSDS2024, Nizza, France.
- | Explainable Outlier Detection for Multivariate Functional Data at reposiTUm , opens an external URL in a new windowRadojičić, U., Mayrhofer, M., & Filzmoser, P. (2024, October 25). Explainable Outlier Detection for Multivariate Functional Data. Turku Applied Mathematics and Statistics Seminar, Finland.
- | Explainable outlier detection for multivariate functional data based on a functional Mahalanobis distance at reposiTUm , opens an external URL in a new windowRadojicic, U., Mayrhofer, M., & Filzmoser, P. (2024, October 3). Explainable outlier detection for multivariate functional data based on a functional Mahalanobis distance. Statistical seminar of Department of Mathematics, Croatia.
- | Robust covariance estimation for matrix-valued data at reposiTUm , opens an external URL in a new windowMayrhofer, M., Radojicic, U., & Filzmoser, P. (2024, August 13). Robust covariance estimation for matrix-valued data. Bernoulli-ims 11th World Congress in Probability and Statistics, Bochum, Germany.
- | Robust PCA and explainable outlier detection for multivariate functional data based on a functional Mahalanobis distance at reposiTUm , opens an external URL in a new windowMayrhofer, M., Radojičić, U., & Filzmoser, P. (2024, July 31). Robust PCA and explainable outlier detection for multivariate functional data based on a functional Mahalanobis distance. ICORS meets DSSV 2024, United States of America (the).
- | Explainable anomaly detection using Shapley values at reposiTUm , opens an external URL in a new windowMayrhofer, M., Radojicic, U., & Filzmoser, P. (2024, April 4). Explainable anomaly detection using Shapley values. Statistiktage 2024, Wien, Austria.
- | A minimum covariance determinant approach for matrix-variate data at reposiTUm , opens an external URL in a new windowMayrhofer, M., Radojicic, U., & Filzmoser, P. (2024). A minimum covariance determinant approach for matrix-variate data. In Statistische Woche 2024: Book of Abstracts (pp. 88–88).
- | Explainable outlier identification for matrix-valued observations at reposiTUm , opens an external URL in a new windowFilzmoser, P., Mayrhofer, M., Radojicic, U., & Lewitschnig, H. (2023). Explainable outlier identification for matrix-valued observations. In Book of Abstracts : International Conference on Data Science : ICDS 2023 : Multidimensional Perspectives: From Statistical Learning to Data Science Applications (pp. 13–13).
- | New Mission Profile Model Using Functional Data Analysis at reposiTUm , opens an external URL in a new windowMayrhofer, M., Lewitschnig, H., & Filzmoser, P. (2023, October 19). New Mission Profile Model Using Functional Data Analysis. Infineon meets University 2023, Germany.
- | Outlier explanation based on Shapley values for vector- and matrix-valued observations at reposiTUm , opens an external URL in a new windowFilzmoser, P., & Mayrhofer, M. (2023). Outlier explanation based on Shapley values for vector- and matrix-valued observations. In P. Coretto, G. N. Giordano, & M. La Rocca (Eds.), CLADAC 2023 : Book of Abstracts and Short Papers : 14th Scientific Meeting of the Classification and Data Analysis Group (pp. 156–158). Pearson Education Resources.
- | Explainable outlier detection based on Shapley values at reposiTUm , opens an external URL in a new windowMayrhofer, M., & Filzmoser, P. (2023). Explainable outlier detection based on Shapley values. In PROGRAMME AND ABSTRACTS 25th International Conference on Computational Statistics (COMPSTAT 2023) (pp. 13–14).
- | L0 Regularized Cellwise Outlier Detection and Covariance Estimation at reposiTUm , opens an external URL in a new windowMayrhofer, M., Rieser, C., & Filzmoser, P. (2023). L0 Regularized Cellwise Outlier Detection and Covariance Estimation. In Book of abstracts: Joint conference of Data Science, Statistics & Visualisation and the European Conference on Data Analysis (pp. 95–95).
- | Explainable Multivariate Outlier Detection based on Shapley Values at reposiTUm , opens an external URL in a new windowMayrhofer, M., & Filzmoser, P. (2023). Explainable Multivariate Outlier Detection based on Shapley Values. In Book of Abstracts Olomoucian Days of Applied Mathematics ODAM 2023 (pp. 53–53).
- | Outlier detection and explanation for matrix-valued data at reposiTUm , opens an external URL in a new windowMayrhofer, M., Radojicic, U., Lewitschnig, H., & Filzmoser, P. (2023, May 24). Outlier detection and explanation for matrix-valued data. International Conference on Robust Statistics (ICORS) - 2023, Toulouse, France.
- | Outlier explanation using Shapley values and Mahalanobis distances at reposiTUm , opens an external URL in a new windowMayrhofer, M., & Filzmoser, P. (2022, July 6). Outlier explanation using Shapley values and Mahalanobis distances. International Conference on Robust Statistics (ICORS 2022), Waterloo, Canada.