24 citations · 157 across the 42 of their papers we have counts for
44 papers
How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection
Lorenzo Guerra, Thomas Chapuis, Guillaume Duc +2
Provenance-based intrusion detection systems (PIDS) frequently report strong performance, but the conclusions drawn from these results can be highly sensitive to benchmarking choic…
Polar Depth for Potentially Heavy-Tailed Data
Stephan Clemençon, Carlos Fernándes, Pavlo Mozharovskyi +1
Motivated by the analysis of the behaviour of extremes from multivariate heavy-tailed distributions, we introduce a novel notion of statistical depth, referred to as Polar Depth. T…
Memory Constrained Dynamic Subnetwork Update for Transfer Learning
Aël Quélennec, Pavlo Mozharovskyi, Van-Tam Nguyen +1
On-device neural network training faces critical memory constraints that limit the adaptation of pre-trained models to downstream tasks. We present MeDyate, a theoretically-grounde…
Study of Training Dynamics for Memory-Constrained Fine-Tuning
Aël Quélennec, Nour Hezbri, Pavlo Mozharovskyi +2
Memory-efficient training of deep neural networks has become increasingly important as models grow larger while deployment environments impose strict resource constraints. We propo…
Self-Supervised Learning of Graph Representations for Network Intrusion Detection
Lorenzo Guerra, Thomas Chapuis, Guillaume Duc +2
Detecting intrusions in network traffic is a challenging task, particularly under limited supervision and constantly evolving attack patterns. While recent works have leveraged gra…
Data Depth as a Risk
Arturo Castellanos, Pavlo Mozharovskyi
Data depths are score functions that quantify in an unsupervised fashion how central is a point inside a distribution, with numerous applications such as anomaly detection, multiva…
Kernel Trace Distance: Quantum Statistical Metric between Measures through RKHS Density Operators
Arturo Castellanos, Anna Korba, Pavlo Mozharovskyi +1
Distances between probability distributions are a key component of many statistical machine learning tasks, from two-sample testing to generative modeling, among others. We introdu…
Model-Free Kernel Conformal Depth Measures Algorithm for Uncertainty Quantification in Regression Models in Separable Hilbert Spaces
Marcos Matabuena, Rahul Ghosal, Pavlo Mozharovskyi +2
Depth measures are powerful tools for defining level sets in emerging, non--standard, and complex random objects such as high-dimensional multivariate data, functional data, and ra…
Massive parallelization of projection-based depths
Leonardo Leone, Pavlo Mozharovskyi, David Bounie
This article introduces a novel methodology for the massive parallelization of projection-based depths, addressing the computational challenges of data depth in high-dimensional sp…
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 +4
The integration of digital devices in modern vehicles has revolutionized automotive technology, enhancing safety and the overall driving experience. The Controller Area Network (CA…
Restyling Unsupervised Concept Based Interpretable Networks with Generative Models
Jayneel Parekh, Quentin Bouniot, Pavlo Mozharovskyi +2
Developing inherently interpretable models for prediction has gained prominence in recent years. A subclass of these models, wherein the interpretable network relies on learning hi…
Conformal uncertainty quantification using kernel depth measures in separable Hilbert spaces
Marcos Matabuena, Rahul Ghosal, Pavlo Mozharovskyi +2
Depth measures have gained popularity in the statistical literature for defining level sets in complex data structures like multivariate data, functional data, and graphs. Despite…
Fast kernel half-space depth for data with non-convex supports
Arturo Castellanos, Pavlo Mozharovskyi, Florence d'Alché-Buc +1
Data depth is a statistical function that generalizes order and quantiles to the multivariate setting and beyond, with applications spanning over descriptive and visual statistics,…
Towards On-device Learning on the Edge: Ways to Select Neurons to Update under a Budget Constraint
Aël Quélennec, Enzo Tartaglione, Pavlo Mozharovskyi +1
In the realm of efficient on-device learning under extreme memory and computation constraints, a significant gap in successful approaches persists. Although considerable effort has…
Abnormal component analysis
Romain Valla, Pavlo Mozharovskyi, Florence d'Alché-Buc
At the crossway of machine learning and data analysis, anomaly detection aims at identifying observations that exhibit abnormal behaviour. Be it measurement errors, disease develop…
Tailoring Mixup to Data for Calibration
Quentin Bouniot, Pavlo Mozharovskyi, Florence d'Alché-Buc
Among all data augmentation techniques proposed so far, linear interpolation of training samples, also called Mixup, has found to be effective for a large panel of applications. Al…
Tackling Interpretability in Audio Classification Networks with Non-negative Matrix Factorization
Jayneel Parekh, Sanjeel Parekh, Pavlo Mozharovskyi +2
This paper tackles two major problem settings for interpretability of audio processing networks, post-hoc and by-design interpretation. For post-hoc interpretation, we aim to inter…
Optimized preprocessing and Tiny ML for Attention State Classification
Yinghao Wang, Rémi Nahon, Enzo Tartaglione +2
In this paper, we present a new approach to mental state classification from EEG signals by combining signal processing techniques and machine learning (ML) algorithms. We evaluate…
Anomaly detection using data depth: multivariate case
Pavlo Mozharovskyi, Romain Valla
Anomaly detection is a branch of data analysis and machine learning which aims at identifying observations that exhibit abnormal behaviour. Be it measurement errors, disease develo…
Statistical process monitoring of artificial neural networks
Anna Malinovskaya, Pavlo Mozharovskyi, Philipp Otto
The rapid advancement of models based on artificial intelligence demands innovative monitoring techniques which can operate in real time with low computational costs. In machine le…
On exact computation of Tukey depth central regions
Vít Fojtík, Petra Laketa, Pavlo Mozharovskyi +1
The Tukey (or halfspace) depth extends nonparametric methods toward multivariate data. The multivariate analogues of the quantiles are the central regions of the Tukey depth, defin…
Listen to Interpret: Post-hoc Interpretability for Audio Networks with NMF
Jayneel Parekh, Sanjeel Parekh, Pavlo Mozharovskyi +2
This paper tackles post-hoc interpretability for audio processing networks. Our goal is to interpret decisions of a network in terms of high-level audio objects that are also liste…
Statistical Depth Functions for Ranking Distributions: Definitions, Statistical Learning and Applications
Morgane Goibert, Stéphan Clémençon, Ekhine Irurozki +1
The concept of median/consensus has been widely investigated in order to provide a statistical summary of ranking data, i.e. realizations of a random permutation of a finite se…
Functional Anomaly Detection: a Benchmark Study
Guillaume Staerman, Eric Adjakossa, Pavlo Mozharovskyi +3
The increasing automation in many areas of the Industry expressly demands to design efficient machine-learning solutions for the detection of abnormal events. With the ubiquitous d…