24 citations · 115 across the 57 of their papers we have counts for
64 papers
SoK: Rethinking Membership Inference in Tabular Machine Learning
Cristina Pêra, Tânia Carvalho, Maxime Cordy +1
Membership Inference Attacks (MIAs) are currently the dominant approach to evaluating privacy in machine learning applications. Despite their significance, several concerns remain…
Feature-Context Consistency: Unsupervised Adversarial Detection with Drift Stability on Attributed Graphs
Firas Bayram, Maxime Cordy
Adversarial detection on evolving attributed graphs faces two challenges: fraud labels arrive late or not at all, and natural distributional drift erodes any trained decision bound…
Breaking Windows Malware Detection: A Comprehensive Evaluation of Problem-Space Adversarial Robustness
Mashal Zainab, Salijona Dyrmishi, Hamid Bostani +2
Problem-space evasion attacks have exposed critical weaknesses in machine learning-based malware detectors; yet, their evaluation remains fragmented across models, datasets, and at…
Robustness and Trade-offs for Code LLMs on Protected Code
Jin Wen, Yuejun Guo, Yujie Ma +2
Code large language models (LLMs) are increasingly used on software artifacts that may be intentionally obfuscated for intellectual-property protection, reverse-engineering resista…
Learning Generalizable Multimodal Representations for Software Vulnerability Detection
Zeming Dong, Yuejun Guo, Qiang Hu +5
Source code and its accompanying comments are complementary yet naturally aligned modalities-code encodes structural logic while comments capture developer intent. However, existin…
When Prompt Under-Specification Improves Code Correctness: An Exploratory Study of Prompt Wording and Structure Effects on LLM-Based Code Generation
Amal AKLI, Mike PAPADAKIS, Maxime CORDY +1
Large language models are increasingly used for code generation, yet the correctness of their outputs depends not only on model capability but also on how tasks are specified. Prio…
Defective Task Descriptions in LLM-Based Code Generation: Detection and Analysis
Amal Akli, Mike Papadakis, Maxime Cordy +1
Large language models are widely used for code generation, yet they rely on an implicit assumption that the task descriptions are sufficiently detailed and well-formed. However, in…
One Model, Many Skills: Parameter-Efficient Fine-Tuning for Multitask Code Analysis
Amal Akli, Maxime Cordy, Mike Papadakis +1
Large language models have recently surpassed specialized systems on code generation, yet their effectiveness on other code-analysis tasks remains less clear. At the same time, mul…
SoK: Challenges in Tabular Membership Inference Attacks
Cristina Pêra, Tânia Carvalho, Maxime Cordy +1
Membership Inference Attacks (MIAs) are currently a dominant approach for evaluating privacy in machine learning applications. Despite their significance in identifying records bel…
When Prompts Go Wrong: Evaluating Code Model Robustness to Ambiguous, Contradictory, and Incomplete Task Descriptions
Maya Larbi, Amal Akli, Mike Papadakis +4
Large Language Models (LLMs) have demonstrated impressive performance in code generation tasks under idealized conditions, where task descriptions are clear and precise. However, i…
Insights on Adversarial Attacks for Tabular Machine Learning via a Systematic Literature Review
Salijona Dyrmishi, Mohamed Djilani, Thibault Simonetto +2
Adversarial attacks in machine learning have been extensively reviewed in areas like computer vision and NLP, but research on tabular data remains scattered. This paper provides th…
Leveraging External Factors in Household-Level Electrical Consumption Forecasting using Hypernetworks
Fabien Bernier, Maxime Cordy, Yves Le Traon
Accurate electrical consumption forecasting is crucial for efficient energy management and resource allocation. While traditional time series forecasting relies on historical patte…
S2ST-Omni: Hierarchical Language-Aware SpeechLLM Adaptation for Multilingual Speech-to-Speech Translation
Yu Pan, Xiongfei Wu, Yuguang Yang +3
Despite recent advances in speech-to-speech translation (S2ST), it remains difficult to achieve both high translation accuracy and practical flexibility. In this paper, we present…
On the Robustness of Tabular Foundation Models: Test-Time Attacks and In-Context Defenses
Mohamed Djilani, Thibault Simonetto, Karim Tit +4
Recent tabular Foundational Models (FM) such as TabPFN and TabICL, leverage in-context learning to achieve strong performance without gradient updates or fine-tuning. However, thei…
Foundation Models for Autonomous Driving System: An Initial Roadmap
Xiongfei Wu, Mingfei Cheng, Xiaoning Ren +8
Recent advances in foundation models (FMs), including large language models (LLMs), vision-language models (VLMs), and world models, have opened new opportunities for autonomous dr…
PowerGraph-LLM: Novel Power Grid Graph Embedding and Optimization with Large Language Models
Fabien Bernier, Jun Cao, Maxime Cordy +1
Efficiently solving Optimal Power Flow (OPF) problems in power systems is crucial for operational planning and grid management. There is a growing need for scalable algorithms capa…
RobustBlack: Challenging Black-Box Adversarial Attacks on State-of-the-Art Defenses
Mohamed Djilani, Salah Ghamizi, Maxime Cordy
Although adversarial robustness has been extensively studied in white-box settings, recent advances in black-box attacks (including transfer- and query-based approaches) are primar…
TabularBench: Benchmarking Adversarial Robustness for Tabular Deep Learning in Real-world Use-cases
Thibault Simonetto, Salah Ghamizi, Maxime Cordy
While adversarial robustness in computer vision is a mature research field, fewer researchers have tackled the evasion attacks against tabular deep learning, and even fewer investi…
Robustness Analysis of AI Models in Critical Energy Systems
Pantelis Dogoulis, Matthieu Jimenez, Salah Ghamizi +2
This paper analyzes the robustness of state-of-the-art AI-based models for power grid operations under the security criterion. While these models perform well in regular grid…
Constrained Adaptive Attack: Effective Adversarial Attack Against Deep Neural Networks for Tabular Data
Thibault Simonetto, Salah Ghamizi, Maxime Cordy
State-of-the-art deep learning models for tabular data have recently achieved acceptable performance to be deployed in industrial settings. However, the robustness of these models…
Evaluation and Improvement of Fault Detection for Large Language Models
Qiang Hu, Jin Wen, Maxime Cordy +4
Large language models (LLMs) have recently achieved significant success across various application domains, garnering substantial attention from different communities. Unfortunatel…
GenCode: A Generic Data Augmentation Framework for Boosting Deep Learning-Based Code Understanding
Zeming Dong, Qiang Hu, Xiaofei Xie +4
Pre-trained code models lead the era of code intelligence, with multiple models designed with impressive performance. However, one important problem, data augmentation for code dat…
Constrained Adaptive Attacks: Realistic Evaluation of Adversarial Examples and Robust Training of Deep Neural Networks for Tabular Data
Thibault Simonetto, Salah Ghamizi, Antoine Desjardins +2
State-of-the-art deep learning models for tabular data have recently achieved acceptable performance to be deployed in industrial settings. However, the robustness of these models…
Hazards in Deep Learning Testing: Prevalence, Impact and Recommendations
Salah Ghamizi, Maxime Cordy, Yuejun Guo +2
Much research on Machine Learning testing relies on empirical studies that evaluate and show their potential. However, in this context empirical results are sensitive to a number o…