24 citations · 73 across the 16 of their papers we have counts for
17 papers
Signature Isolation Forest
Marta Campi, Guillaume Staerman, Gareth W. Peters +1
Functional Isolation Forest (FIF) is a recent state-of-the-art Anomaly Detection (AD) algorithm designed for functional data. It relies on a tree partition procedure where an abnor…
Enhanced Hallucination Detection in Neural Machine Translation through Simple Detector Aggregation
Anas Himmi, Guillaume Staerman, Marine Picot +2
Hallucinated translations pose significant threats and safety concerns when it comes to the practical deployment of machine translation systems. Previous research works have identi…
Toward Stronger Textual Attack Detectors
Pierre Colombo, Marine Picot, Nathan Noiry +2
The landscape of available textual adversarial attacks keeps growing, posing severe threats and raising concerns regarding the deep NLP system's integrity. However, the crucial pro…
A Novel Information-Theoretic Objective to Disentangle Representations for Fair Classification
Pierre Colombo, Nathan Noiry, Guillaume Staerman +1
One of the pursued objectives of deep learning is to provide tools that learn abstract representations of reality from the observation of multiple contextual situations. More preci…
A Functional Data Perspective and Baseline On Multi-Layer Out-of-Distribution Detection
Eduardo Dadalto, Pierre Colombo, Guillaume Staerman +2
A key feature of out-of-distribution (OOD) detection is to exploit a trained neural network by extracting statistical patterns and relationships through the multi-layer classifier…
Hypothesis Transfer Learning with Surrogate Classification Losses: Generalization Bounds through Algorithmic Stability
Anass Aghbalou, Guillaume Staerman
Hypothesis transfer learning (HTL) contrasts domain adaptation by allowing for a previous task leverage, named the source, into a new one, the target, without requiring access to t…