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Pavlo Mozharovskyi
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2020 – today
- 2024
- [j9]Vít Fojtík, Petra Laketa, Pavlo Mozharovskyi, Stanislav Nagy
:
On Exact Computation of Tukey Depth Central Regions. J. Comput. Graph. Stat. 33(2): 699-713 (2024) - [j8]Jayneel Parekh, Sanjeel Parekh, Pavlo Mozharovskyi
Tackling Interpretability in Audio Classification Networks With Non-negative Matrix Factorization. IEEE ACM Trans. Audio Speech Lang. Process. 32: 1392-1405 (2024) - [j7]Anna Malinovskaya
Statistical Process Monitoring of Artificial Neural Networks. Technometrics 66(1): 104-117 (2024) - [j6]Guillaume Staerman, Pavlo Mozharovskyi, Pierre Colombo, Stéphan Clémençon, Florence d'Alché-Buc:
A Pseudo-Metric between Probability Distributions based on Depth-Trimmed Regions. Trans. Mach. Learn. Res. 2024 (2024) - [c12]Lorenzo Guerra
AI-Driven Intrusion Detection Systems (IDS) on the ROAD Dataset: A Comparative Analysis for Automotive Controller Area Network (CAN). CSCS 2024: 39-49 - [c11]Aël Quélennec
Towards On-Device Learning on the Edge: Ways to Select Neurons to Update Under a Budget Constraint. WACV (Workshops) 2024: 685-694 - [i21]Jayneel Parekh, Quentin Bouniot, Pavlo Mozharovskyi, Alasdair Newson, Florence d'Alché-Buc:
Restyling Unsupervised Concept Based Interpretable Networks with Generative Models. CoRR abs/2407.01331 (2024) - [i20]Lorenzo Guerra, Linhan Xu, Pavlo Mozharovskyi, Paolo Bellavista, Thomas Chapuis, Guillaume Duc, Van-Tam Nguyen:
AI-Driven Intrusion Detection Systems (IDS) on the ROAD dataset: A Comparative Analysis for automotive Controller Area Network (CAN). CoRR abs/2408.17235 (2024) - 2023
- [j5]Guillaume Staerman, Eric Adjakossa, Pavlo Mozharovskyi, Vera Hofer, Jayant Sen Gupta, Stéphan Clémençon:
Functional anomaly detection: a benchmark study. Int. J. Data Sci. Anal. 16(1): 101-117 (2023) - [c10]Yinghao Wang, Rémi Nahon
Optimized preprocessing and Tiny ML for Attention State Classification. SSP 2023: 695-699 - [i19]Yinghao Wang, Rémi Nahon
Optimized preprocessing and Tiny ML for Attention State Classification. CoRR abs/2303.11371 (2023) - [i18]Jayneel Parekh, Sanjeel Parekh, Pavlo Mozharovskyi, Gaël Richard, Florence d'Alché-Buc:
Tackling Interpretability in Audio Classification Networks with Non-negative Matrix Factorization. CoRR abs/2305.07132 (2023) - [i17]Quentin Bouniot, Pavlo Mozharovskyi, Florence d'Alché-Buc:
Tailoring Mixup to Data using Kernel Warping functions. CoRR abs/2311.01434 (2023) - [i16]Aël Quélennec, Enzo Tartaglione, Pavlo Mozharovskyi, Van-Tam Nguyen:
Towards On-device Learning on the Edge: Ways to Select Neurons to Update under a Budget Constraint. CoRR abs/2312.05282 (2023) - [i15]Arturo Castellanos, Pavlo Mozharovskyi, Florence d'Alché-Buc, Hicham Janati:
Fast kernel half-space depth for data with non-convex supports. CoRR abs/2312.14136 (2023) - [i14]Romain Valla, Pavlo Mozharovskyi, Florence d'Alché-Buc:
Anomaly component analysis. CoRR abs/2312.16139 (2023) - 2022
- [b2]Pavlo Mozharovskyi:
Data depth: computation, applications, and beyond. Polytechnic Institute of Paris, France, 2022 - [c9]Morgane Goibert, Stéphan Clémençon, Ekhine Irurozki, Pavlo Mozharovskyi:
Statistical Depth Functions for Ranking Distributions: Definitions, Statistical Learning and Applications. AISTATS 2022: 10376-10406 - [c8]Jayneel Parekh, Sanjeel Parekh, Pavlo Mozharovskyi, Florence d'Alché-Buc, Gaël Richard:
Listen to Interpret: Post-hoc Interpretability for Audio Networks with NMF. NeurIPS 2022 - [i13]Guillaume Staerman, Eric Adjakossa, Pavlo Mozharovskyi, Vera Hofer, Jayant Sen Gupta, Stéphan Clémençon:
Functional Anomaly Detection: a Benchmark Study. CoRR abs/2201.05115 (2022) - [i12]Morgane Goibert, Stéphan Clémençon, Ekhine Irurozki, Pavlo Mozharovskyi:
Statistical Depth Functions for Ranking Distributions: Definitions, Statistical Learning and Applications. CoRR abs/2201.08105 (2022) - [i11]Jayneel Parekh, Sanjeel Parekh, Pavlo Mozharovskyi, Florence d'Alché-Buc, Gaël Richard:
Listen to Interpret: Post-hoc Interpretability for Audio Networks with NMF. CoRR abs/2202.11479 (2022) - [i10]Anna Malinovskaya, Pavlo Mozharovskyi, Philipp Otto
Statistical monitoring of models based on artificial intelligence. CoRR abs/2209.07436 (2022) - [i9]Pavlo Mozharovskyi:
Anomaly detection using data depth: multivariate case. CoRR abs/2210.02851 (2022) - 2021
- [j4]Rainer Dyckerhoff
Approximate computation of projection depths. Comput. Stat. Data Anal. 157: 107166 (2021) - [c7]Guillaume Staerman, Pierre Laforgue, Pavlo Mozharovskyi, Florence d'Alché-Buc:
When OT meets MoM: Robust estimation of Wasserstein Distance. AISTATS 2021: 136-144 - [c6]Jayneel Parekh, Pavlo Mozharovskyi, Florence d'Alché-Buc:
A Framework to Learn with Interpretation. NeurIPS 2021: 24273-24285 - [i8]Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon, Florence d'Alché-Buc:
Depth-based pseudo-metrics between probability distributions. CoRR abs/2103.12711 (2021) - [i7]Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon:
Affine-Invariant Integrated Rank-Weighted Depth: Definition, Properties and Finite Sample Analysis. CoRR abs/2106.11068 (2021) - 2020
- [j3]Oleg Badunenko, Pavlo Mozharovskyi
Statistical inference for the Russell measure of technical efficiency. J. Oper. Res. Soc. 71(3): 517-527 (2020) - [c5]Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon:
The Area of the Convex Hull of Sampled Curves: a Robust Functional Statistical Depth measure. AISTATS 2020: 570-579 - [c4]Valérie Beaudouin, Isabelle Bloch, David Bounie, Stéphan Clémençon, Florence d'Alché-Buc, James Eagan, Winston Maxwell, Pavlo Mozharovskyi, Jayneel Parekh:
Identifying the "right" level of explanation in a given situation. NeHuAI@ECAI 2020: 63-66 - [i6]Valérie Beaudouin, Isabelle Bloch, David Bounie, Stéphan Clémençon, Florence d'Alché-Buc, James Eagan, Winston Maxwell, Pavlo Mozharovskyi, Jayneel Parekh:
Flexible and Context-Specific AI Explainability: A Multidisciplinary Approach. CoRR abs/2003.07703 (2020) - [i5]Guillaume Staerman, Pierre Laforgue, Pavlo Mozharovskyi, Florence d'Alché-Buc:
When OT meets MoM: Robust estimation of Wasserstein Distance. CoRR abs/2006.10325 (2020) - [i4]Jayneel Parekh, Pavlo Mozharovskyi, Florence d'Alché-Buc:
A Framework to Learn with Interpretation. CoRR abs/2010.09345 (2020)
2010 – 2019
- 2019
- [c3]Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon, Florence d'Alché-Buc:
Functional Isolation Forest. ACML 2019: 332-347 - [i3]Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon, Florence d'Alché-Buc:
Functional Isolation Forest. CoRR abs/1904.04573 (2019) - [i2]Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon:
The Area of the Convex Hull of Sampled Curves: a Robust Functional Statistical Depth Measure. CoRR abs/1910.04085 (2019) - 2016
- [j2]Rainer Dyckerhoff
Exact computation of the halfspace depth. Comput. Stat. Data Anal. 98: 19-30 (2016) - 2015
- [b1]Pavlo Mozharovskyi:
Contributions to depth-based classification and computation of the Tukey depth. University of Cologne, Kovač 2015, ISBN 978-3-8300-8213-2, pp. 1-188 - [j1]Pavlo Mozharovskyi
Classifying real-world data with the DDα-procedure. Adv. Data Anal. Classif. 9(3): 287-314 (2015) - 2012
- [c2]Tatjana Lange, Karl Mosler, Pavlo Mozharovskyi
DDα-Classification of Asymmetric and Fat-Tailed Data. GfKl 2012: 71-78 - [c1]Tatjana Lange, Pavlo Mozharovskyi
The Alpha-Procedure: A Nonparametric Invariant Method for Automatic Classification of Multi-Dimensional Objects. GfKl 2012: 79-86 - [i1]Tatjana Lange, Karl Mosler, Pavlo Mozharovskyi:
Fast nonparametric classification based on data depth. CoRR abs/1207.4992 (2012)
Coauthor Index
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last updated on 2024-12-02 22:28 CET by the dblp team
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