The Alpha-Procedure: A Nonparametric Invariant Method for Automatic Classification of Multi-Dimensional Objects. - Tatjana Lange; Pavlo Mozharovskyi (GfKl, 2012)

DDα-Classification of Asymmetric and Fat-Tailed Data. - Tatjana Lange; Karl Mosler; Pavlo Mozharovskyi (GfKl, 2012)

Fast nonparametric classification based on data depth - Tatjana Lange; Karl Mosler; Pavlo Mozharovskyi (CoRR, 2012)

Classifying real-world data with the DDα-procedure. - Pavlo Mozharovskyi; Karl Mosler; Tatjana Lange (Adv. Data Anal. Classif., 2015)

Contributions to depth-based classification and computation of the Tukey depth. - Pavlo Mozharovskyi (Kovač, 2015)

Exact computation of the halfspace depth. - Rainer Dyckerhoff; Pavlo Mozharovskyi (Comput. Stat. Data Anal., 2016)

Functional Isolation Forest. - Guillaume Staerman; Pavlo Mozharovskyi; Stéphan Clémençon; Florence d'Alché-Buc (ACML, 2019)

The Area of the Convex Hull of Sampled Curves: a Robust Functional Statistical Depth measure. - Guillaume Staerman; Pavlo Mozharovskyi; Stéphan Clémençon (AISTATS, 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 (NeHuAI@ECAI, 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)

Statistical inference for the Russell measure of technical efficiency. - Oleg Badunenko; Pavlo Mozharovskyi (J. Oper. Res. Soc., 2020)

When OT meets MoM: Robust estimation of Wasserstein Distance. - Guillaume Staerman; Pierre Laforgue; Pavlo Mozharovskyi; Florence d'Alché-Buc (AISTATS, 2021)

A Framework to Learn with Interpretation. - Jayneel Parekh; Pavlo Mozharovskyi; Florence d'Alché-Buc (NeurIPS, 2021)

Depth-based pseudo-metrics between probability distributions. - Guillaume Staerman; Pavlo Mozharovskyi; Stéphan Clémençon; Florence d'Alché-Buc (CoRR, 2021)

Affine-Invariant Integrated Rank-Weighted Depth: Definition, Properties and Finite Sample Analysis. - Guillaume Staerman; Pavlo Mozharovskyi; Stéphan Clémençon (CoRR, 2021)

Approximate computation of projection depths. - Rainer Dyckerhoff; Pavlo Mozharovskyi; Stanislav Nagy (Comput. Stat. Data Anal., 2021)

Data depth: computation, applications, and beyond. - Pavlo Mozharovskyi (2022)

Statistical Depth Functions for Ranking Distributions: Definitions, Statistical Learning and Applications. - Morgane Goibert; Stéphan Clémençon; Ekhine Irurozki; Pavlo Mozharovskyi (AISTATS, 2022)

Listen to Interpret: Post-hoc Interpretability for Audio Networks with NMF. - Jayneel Parekh; Sanjeel Parekh; Pavlo Mozharovskyi; Florence d'Alché-Buc; Gaël Richard (NeurIPS, 2022)

Statistical monitoring of models based on artificial intelligence. - Anna Malinovskaya; Pavlo Mozharovskyi; Philipp Otto (CoRR, 2022)

Anomaly detection using data depth: multivariate case. - Pavlo Mozharovskyi (CoRR, 2022)

Optimized preprocessing and Tiny ML for Attention State Classification. - Yinghao Wang; Rémi Nahon; Enzo Tartaglione; Pavlo Mozharovskyi; Van-Tam Nguyen (SSP, 2023)

Tailoring Mixup to Data using Kernel Warping functions. - Quentin Bouniot; Pavlo Mozharovskyi; Florence d'Alché-Buc (CoRR, 2023)

Fast kernel half-space depth for data with non-convex supports. - Arturo Castellanos 002; Pavlo Mozharovskyi; Florence d'Alché-Buc; Hicham Janati (CoRR, 2023)

Anomaly component analysis. - Romain Valla; Pavlo Mozharovskyi; Florence d'Alché-Buc (CoRR, 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., 2023)

AI-Driven Intrusion Detection Systems (IDS) on the ROAD Dataset: A Comparative Analysis for Automotive Controller Area Network (CAN). - Lorenzo Guerra; Linhan Xu; Paolo Bellavista; Thomas Chapuis; Guillaume Duc; Pavlo Mozharovskyi; Van-Tam Nguyen (CSCS, 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 (WACV (Workshops), 2024)

On Exact Computation of Tukey Depth Central Regions. - Vít Fojtík; Petra Laketa; Pavlo Mozharovskyi; Stanislav Nagy (J. Comput. Graph. Stat., 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)

Statistical Process Monitoring of Artificial Neural Networks. - Anna Malinovskaya; Pavlo Mozharovskyi; Philipp Otto (Technometrics, 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)

Tailoring Mixup to Data for Calibration. - Quentin Bouniot; Pavlo Mozharovskyi; Florence d'Alché-Buc (ICLR, 2025)

Restyling Unsupervised Concept Based Interpretable Networks with Generative Models. - Jayneel Parekh; Quentin Bouniot; Pavlo Mozharovskyi; Alasdair Newson; Florence d'Alché-Buc (ICLR, 2025)

Model-Free Kernel Conformal Depth Measures Algorithm for Uncertainty Quantification in Regression Models in Separable Hilbert Spaces. - Marcos Matabuena; Rahul Ghosal; Pavlo Mozharovskyi; Oscar Hernan Madrid Padilla; Jukka-Pekka Onnela (CoRR, 2025)

Kernel Trace Distance: Quantum Statistical Metric between Measures through RKHS Density Operators. - Arturo Castellanos 002; Anna Korba; Pavlo Mozharovskyi; Hicham Janati (CoRR, 2025)

Data Depth as a Risk. - Arturo Castellanos 002; Pavlo Mozharovskyi (CoRR, 2025)