Publications

Journal articles:

  • Staerman, G., Adjakossa, E., Mozharovskyi, P., Hofer, V., Sen Gupta, J., and Clémençon, S. (2022). Functional anomaly detection: a benchmark study. International Journal of Data Science and Analytics, in press. [arXiv:2201.05115]
  • Mosler, K. and Mozharovskyi, P. (2022): Choosing among notions of multivariate depth statistics. Statistical Science, 37(3), 348-368. [arXiv:2004.01927]
  • Lafaye De Micheaux, P., Mozharovskyi, P., and Vimond, M. (2021): Depth for curve data and applications. Journal of the American Statistical Association, 116(536), 1881-1897. [arXiv:1901.00180]
  • Statzer, C., Jongsma, E., Liu, S. X., Dakhovnik, A., Wandrey, F., Mozharovskyi, P., Zülli, F., and Ewald, C. Y. (2021). Youthful and age-related matreotypes predict drugs promoting longevity. Aging Cell, 20, e13441. [https://doi.org/10.1111/acel.13441]
  • Dyckerhoff, R., Mozharovskyi, P., and Nagy, S. (2021): Approximate computation of projection depths. Computational Statistics and Data Analysis, 157, 107166. [arXiv:2007.08016] [Reproducing codes] [Experimental results]
  • Nagy, S., Dyckerhoff, R., and Mozharovskyi, P. (2020): Uniform convergence rates for the approximated halfspace and projection depth. Electronic Journal of Statistics, 14(2), 3939-3975. [arXiv:1910.05956]
  • Mozharovskyi, P., Josse, J., and Husson, F. (2020): Nonparametric imputation by data depth. Journal of the American Statistical Association, 115(529), 241-253. [arXiv:1701.03513] [R-package and reproducing scripts]
  • Badunenko, O. and Mozharovskyi, P. (2020): Statistical inference for the Russel measure of technical efficiency. Journal of the Operational Research Society, 71(3), 517-527. [PDF]
  • Pokotylo, O., Mozharovskyi, P., and Dyckerhoff, R. (2019): Depth and depth-based classification with R-package ddalpha. Journal of Statistical Software, 91(5), 1-46. [arXiv:1608.04109]
  • Liu, X., Mosler, K., and Mozharovskyi, P. (2019): Fast computation of Tukey trimmed regions and median in dimension p≥2. Journal of Computational and Graphical Statistics, 28(3), 682–697. [arXiv:1412.5122] [Reproducing scripts] [R-package TukeyRegion]
  • Mosler, K. and Mozharovskyi, P. (2017): Fast DD-classification of functional data. Statistical Papers, 58(4), 1055–1089. [arXiv:1403.1158]
  • Mozharovskyi, P. and Vogler, J. (2016): Composite marginal likelihood estimation of spatial autoregressive probit models feasible in very large samples. Economics Letters, 148, 87–90. [SSRN-id2806151] [Matlab and C++ sources]
  • Badunenko, O. and Mozharovskyi, P. (2016): Nonparametric frontier analysis using STATA. Stata Journal, 16(3), 550–589. [PDF] [STATA and C++ sources]
  • Dyckerhoff, R. and Mozharovskyi, P. (2016): Exact computation of the halfspace depth. Computational Statistics and Data Analysis, 98, 19–30. [arXiv:1411.6927] [C++ sources]
  • Mozharovskyi, P., Mosler, K., and Lange, T. (2015): Classifying real-world data with the DDα-procedure. Advances in Data Analysis and Classification, 9(3), 287–314. [arXiv:1407.5185]
  • Lange, T., Mosler, K., and Mozharovskyi, P. (2014): Fast nonparametric classification based on data depth. Statistical Papers, 55(1), 49–69. [arXiv:1207.4992]
  • Lange, T. and Mozharovskyi, P. (2010): Depth determination for multivariate samples (in Russian). Inductive Modelling of Complex Systems, I 2, 101–119.
  • Grishko, V.F. and Mozharovsky, P.F. (2009): Management-information system hardware reliability evaluation (in Ukrainian). Mathematical Machines and Systems, 3, 194–201.

 

Conference proceedings:

  • Parekh, J., Parekh, S., Mozharovskyi, P., D’Alché-Buc, F., and Richard, G. (2022). Listen to interpret: Post-hoc interpretability for audio networks with NMF. NeurIPS 2022, in press. [arXiv:2202.11479]
  • Goibert, M., Clémençon, S., Irurozki, E., and Mozharovskyi, P. (2021): Statistical depth functions for ranking distributions: definitions, statistical learning and applications. In: Camps-Valls, G., Ruiz, F. J. R., Valera , I. (eds.), Proceedings of The Twenty Fifth International Conference on Artificial Intelligence and Statistics (AISTATS 2022), 151, 10376-10406. [arXiv:2201.08105]
  • Parekh, J., Mozharovskyi, P., and D’Alché-Buc, F. (2021): A framework to learn with interpretation. In: Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P.S. and Wortman Vaughan, J. (eds.), Advances in Neural Information Processing Systems 34 (NeurIPS 2021). [arXiv:2010.09345]
  • Staerman, G., Laforgue, P., Mozharovskyi, P., and D’Alché-Buc, F. (2021): When OT meets MoM: Robust estimation of Wasserstein distance. In: Banerjee, A. and Fukumizu, K. (eds.), Proceedings of The 24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021), 130, 136-144. [arXiv:2006.10325]
  • Beaudoin, V., Bloch, I., Bounie, D., Clémençon, S., D’Alché-Buc, F., Eagan, J., Maxwell, W., Mozharovskyi, P., and Parekh, J. (2020): Identifying the “right” level of explanation in a given situation. In: Saffiotti, A., Serafini, L., and Lukowicz, P. (eds.), Proceedings of the First International Workshop on New Foundations for Human-Centered AI (NeHuAI 2020 with ECAI 2020), 63-66. [pdf]
  • Staerman, G., Mozharovskyi, P., and Clémençon, S. (2020): The area of the convex hull of sampled curves: a robust functional statistical depth measure. In: Chiappa, S. and Calandra, R. (eds.), Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics (AISTATS 2020), 108, 570-579. [arXiv:1910.04085]
  • Staerman, G., Mozharovskyi, P., Clémençon, S., and D’Alché-Buc, F. (2019): Functional isolation forest. In: Lee, W. S. and Suzuki, T. (eds.), Proceedings of The Eleventh Asian Conference on Machine Learning (ACML 2019), 101, 332-347. [arXiv:1904.04573]
  • Lange, T., Mosler, K., and Mozharovskyi, P. (2014): DDα-classification of asymmetric and fat-tailed data. In: Spiliopoulou, M., Schmidt-Thieme, L., and Janning, R. (eds.), Data Analysis, Machine Learning and Knowledge Discovery, Springer, Berlin, 71–78. [pdf]
  • Lange, T. and Mozharovskyi, P. (2014): The alpha-procedure: a nonparametric invariant method for automatic classification of multi-dimensional objects. In: Spiliopoulou, M., Schmidt-Thieme, L., and Janning, R. (eds.), Data Analysis, Machine Learning and Knowledge Discovery, Springer, Berlin, 79–86. [pdf]
  • Lange, T., Mosler, K., and Mozharovskyi, P. (2013): Efficient depth-based classification using a projective invariant of class membership (in Russian). Control Systems and Computers, 2, 47–58.
  • Lange, T., Mozharovskyi, P., and Barath, G. (2011): Two approaches for solving tasks of pattern recognition and reconstruction of functional dependencies. Proceedings of ASMDA Conference, Rome, 7–10 June 2011 (supplemented with examples and benchmark results, Statistical Week, Leipzig, 19–23 September 2011).
  • Rolick, A., Mozharovskyi, P., and Mart, B. (2010): Application of depth-trimmed regions in IT-infrastructure control systems (in Russian). Coll. of Papers of the 10th Int. Conf. Intellectual Analysis of Information, Kyiv, 18–21 May 2010, 214–221.

 

Scientific reports:

  • Beaudouin, V., Bloch, I., Bounie, D., Clémençon, S., D’Alché-Buc, F., Eagan, J., Maxwell, W., Mozharovskyi, P., and Parekh, J. (2020): Flexible and context-specific AI explainability: A multidisciplinary approach. [arXiv:2003.07703]
  • Mozharovskyi, P. (2016): Tukey depth: linear programming and applications. [arXiv:1603.00069]

 

PhD thesis:

  • Mozharovskyi, P. (2015): Contributions to depth-based classification and computation of the Tukey depth. Dr. Kovač Verlag, Hamburg. [pdf]

 

Habilitation dissertation (HDR):

  • Mozharovskyi, P. (2022): Data depth: computation, applications, and beyond. Institut Polytechnique de Paris, Palaiseau. [pdf]

 

Work in progress:

  • Mozharovskyi, P.: Anomaly detection using data depth: multivariate case. [arXiv:2210.02851]
  • Malinovskaya, A., Mozharovskyi, P., and Otto, P.: Statistical monitoring of models based on artificial intelligence. [arXiv:2209.07436]
  • Fojtík, V., Laketa, P., Mozharovskyi, P., and Nagy, S.: On exact computation of Tukey depth central regions. [arXiv:2208.04587]
  • Staerman, G., Mozharovskyi, P., and Clémençon, S.: Affine-invariant integrated rank-weighted depth: definition, properties and finite sample analysis. [arXiv:2106.11068]
  • Staerman, G., Mozharovskyi, P., Colombo, P., Clémençon, S., and D’Alché-Buc, F.: A pseudo-metric between probability distributions based on depth-trimmed regions. [arXiv:2103.12711]
  • Ivanovs, J. and Mozharovskyi, P.: Distributionally robust halfspace depth. [arXiv:2101.00726]
  • Mozharovskyi, P., Patilea, V., and Rouvière, L.: Simple tests of stability for parametric regressions.