3 papers
cs.CV2026
OPTIMUS-Prime: Minimal and Sufficient Concept Explanations for Deep Vision Models
Arthur Hoarau, Chenrui Zhu, Vu Linh Nguyen
The growing demand for transparency in automated decision-making has propelled eXplainable Artificial Intelligence (XAI) to the forefront of machine learning research. In computer…
cs.LG2026
Reducing Aleatoric and Epistemic Uncertainty through Multi-modal Data Acquisition
Arthur Hoarau, Benjamin Quost, Sébastien Destercke +1
To generate accurate and reliable predictions, modern AI systems need to combine data from multiple modalities, such as text, images, audio, spreadsheets, and time series. Multi-mo…
cs.LG2025
Robust Explanations Through Uncertainty Decomposition: A Path to Trustworthier AI
Chenrui Zhu, Louenas Bounia, Vu Linh Nguyen +2
Recent advancements in machine learning have emphasized the need for transparency in model predictions, particularly as interpretability diminishes when using increasingly complex…