Pavlo Mozharovskyi

According to our database, Pavlo Mozharovskyi authored at least 33 papers between 2012 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

A Pseudo-Metric between Probability Distributions based on Depth-Trimmed Regions.

Guillaume Staerman, Pavlo Mozharovskyi, Pierre Colombo, Stéphan Clémençon, Florence d'Alché-Buc

Trans. Mach. Learn. Res., 2024

Statistical Process Monitoring of Artificial Neural Networks.

Anna Malinovskaya, Pavlo Mozharovskyi, Philipp Otto

Technometrics, 2024

Tackling Interpretability in Audio Classification Networks With Non-negative Matrix Factorization.

Jayneel Parekh, Sanjeel Parekh, Pavlo Mozharovskyi, Gaël Richard, Florence d'Alché-Buc

IEEE ACM Trans. Audio Speech Lang. Process., 2024

On Exact Computation of Tukey Depth Central Regions.

Vít Fojtík, Petra Laketa, Pavlo Mozharovskyi, Stanislav Nagy

J. Comput. Graph. Stat., 2024

AI-Driven Intrusion Detection Systems (IDS) on the ROAD dataset: A Comparative Analysis for automotive Controller Area Network (CAN).

Lorenzo Guerra, Linhan Xu, Pavlo Mozharovskyi, Paolo Bellavista, Thomas Chapuis, Guillaume Duc, Van-Tam Nguyen

CoRR, 2024

Restyling Unsupervised Concept Based Interpretable Networks with Generative Models.

Jayneel Parekh, Quentin Bouniot, Pavlo Mozharovskyi, Alasdair Newson, Florence d'Alché-Buc

CoRR, 2024

Towards On-Device Learning on the Edge: Ways to Select Neurons to Update Under a Budget Constraint.

Aël Quélennec, Enzo Tartaglione, Pavlo Mozharovskyi, Van-Tam Nguyen

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, 2024

2023

Functional anomaly detection: a benchmark study.

Guillaume Staerman, Eric Adjakossa, Pavlo Mozharovskyi, Vera Hofer, Jayant Sen Gupta, Stéphan Clémençon

Int. J. Data Sci. Anal., June, 2023

Anomaly component analysis.

Romain Valla, Pavlo Mozharovskyi, Florence d'Alché-Buc

CoRR, 2023

Fast kernel half-space depth for data with non-convex supports.

Arturo Castellanos, Pavlo Mozharovskyi, Florence d'Alché-Buc, Hicham Janati

CoRR, 2023

Tailoring Mixup to Data using Kernel Warping functions.

Quentin Bouniot, Pavlo Mozharovskyi, Florence d'Alché-Buc

CoRR, 2023

Optimized preprocessing and Tiny ML for Attention State Classification.

Yinghao Wang, Rémi Nahon, Enzo Tartaglione, Pavlo Mozharovskyi, Van-Tam Nguyen

Proceedings of the IEEE Statistical Signal Processing Workshop, 2023

2022

Anomaly detection using data depth: multivariate case.

Pavlo Mozharovskyi

CoRR, 2022

Statistical monitoring of models based on artificial intelligence.

Anna Malinovskaya, Pavlo Mozharovskyi, Philipp Otto

CoRR, 2022

Listen to Interpret: Post-hoc Interpretability for Audio Networks with NMF.

Jayneel Parekh, Sanjeel Parekh, Pavlo Mozharovskyi, Florence d'Alché-Buc, Gaël Richard

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Statistical Depth Functions for Ranking Distributions: Definitions, Statistical Learning and Applications.

Morgane Goibert, Stéphan Clémençon, Ekhine Irurozki, Pavlo Mozharovskyi

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Data depth: computation, applications, and beyond.

Pavlo Mozharovskyi

, 2022

2021

Approximate computation of projection depths.

Rainer Dyckerhoff, Pavlo Mozharovskyi, Stanislav Nagy

Comput. Stat. Data Anal., 2021

Affine-Invariant Integrated Rank-Weighted Depth: Definition, Properties and Finite Sample Analysis.

Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon

CoRR, 2021

Depth-based pseudo-metrics between probability distributions.

Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon, Florence d'Alché-Buc

CoRR, 2021

A Framework to Learn with Interpretation.

Jayneel Parekh, Pavlo Mozharovskyi, Florence d'Alché-Buc

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

When OT meets MoM: Robust estimation of Wasserstein Distance.

Guillaume Staerman, Pierre Laforgue, Pavlo Mozharovskyi, Florence d'Alché-Buc

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Statistical inference for the Russell measure of technical efficiency.

Oleg Badunenko, Pavlo Mozharovskyi

J. Oper. Res. Soc., 2020

Flexible and Context-Specific AI Explainability: A Multidisciplinary Approach.

Valérie Beaudouin, Isabelle Bloch, David Bounie, Stéphan Clémençon, Florence d'Alché-Buc, James Eagan, Winston Maxwell, Pavlo Mozharovskyi, Jayneel Parekh

CoRR, 2020

Identifying the "right" level of explanation in a given situation.

Valérie Beaudouin, Isabelle Bloch, David Bounie, Stéphan Clémençon, Florence d'Alché-Buc, James Eagan, Winston Maxwell, Pavlo Mozharovskyi, Jayneel Parekh

Proceedings of the First International Workshop on New Foundations for Human-Centered AI (NeHuAI) co-located with 24th European Conference on Artificial Intelligence (ECAI 2020), 2020

The Area of the Convex Hull of Sampled Curves: a Robust Functional Statistical Depth measure.

Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Functional Isolation Forest.

Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon, Florence d'Alché-Buc

Proceedings of The 11th Asian Conference on Machine Learning, 2019

2016

Exact computation of the halfspace depth.

Rainer Dyckerhoff, Pavlo Mozharovskyi

Comput. Stat. Data Anal., 2016

2015

Contributions to depth-based classification and computation of the Tukey depth.

Pavlo Mozharovskyi

PhD thesis, 2015

Classifying real-world data with the DDα-procedure.

Pavlo Mozharovskyi, Karl Mosler, Tatjana Lange

Adv. Data Anal. Classif., 2015

2012

Fast nonparametric classification based on data depth

Tatjana Lange, Karl Mosler, Pavlo Mozharovskyi

CoRR, 2012

DD<i>α</i>-Classification of Asymmetric and Fat-Tailed Data.

Tatjana Lange, Karl Mosler, Pavlo Mozharovskyi

Proceedings of the Data Analysis, Machine Learning and Knowledge Discovery, 2012

The Alpha-Procedure: A Nonparametric Invariant Method for Automatic Classification of Multi-Dimensional Objects.

Tatjana Lange, Pavlo Mozharovskyi

Proceedings of the Data Analysis, Machine Learning and Knowledge Discovery, 2012