86 citations · 161 across the 17 of their papers we have counts for
18 papers
REFRESH: Responsible and Efficient Feature Reselection Guided by SHAP Values
Shubham Sharma, Sanghamitra Dutta, Emanuele Albini +3
Feature selection is a crucial step in building machine learning models. This process is often achieved with accuracy as an objective, and can be cumbersome and computationally exp…
Causal Analysis for Robust Interpretability of Neural Networks
Ola Ahmad, Nicolas Bereux, Loïc Baret +2
Interpreting the inner function of neural networks is crucial for the trustworthy development and deployment of these black-box models. Prior interpretability methods focus on corr…
Task Adaptive Feature Transformation for One-Shot Learning
Imtiaz Masud Ziko, Freddy Lecue, Ismail Ben Ayed
We introduce a simple non-linear embedding adaptation layer, which is fine-tuned on top of fixed pre-trained features for one-shot tasks, improving significantly transductive entro…
Rethinking Log Odds: Linear Probability Modelling and Expert Advice in Interpretable Machine Learning
Danial Dervovic, Nicolas Marchesotti, Freddy Lecue +1
We introduce a family of interpretable machine learning models, with two broad additions: Linearised Additive Models (LAMs) which replace the ubiquitous logistic link function in G…
Empowering the trustworthiness of ML-based critical systems through engineering activities
Juliette Mattioli, Agnes Delaborde, Souhaiel Khalfaoui +3
This paper reviews the entire engineering process of trustworthy Machine Learning (ML) algorithms designed to equip critical systems with advanced analytics and decision functions.…
Explainable prediction of Qcodes for NOTAMs using column generation
Krunal Kishor Patel, Guy Desaulniers, Andrea Lodi +1
A NOtice To AirMen (NOTAM) contains important flight route related information. To search and filter them, NOTAMs are grouped into categories called QCodes. In this paper, we devel…
FisheyeHDK: Hyperbolic Deformable Kernel Learning for Ultra-Wide Field-of-View Image Recognition
Ola Ahmad, Freddy Lecue
Conventional convolution neural networks (CNNs) trained on narrow Field-of-View (FoV) images are the state-of-the-art approaches for object recognition tasks. Some methods proposed…
Interpretable Preference-based Reinforcement Learning with Tree-Structured Reward Functions
Tom Bewley, Freddy Lecue
The potential of reinforcement learning (RL) to deliver aligned and performant agents is partially bottlenecked by the reward engineering problem. One alternative to heuristic tria…
Adaptable Deformable Convolutions for Semantic Segmentation of Fisheye Images in Autonomous Driving Systems
Clément Playout, Ola Ahmad, Freddy Lecue +1
Advanced Driver-Assistance Systems rely heavily on perception tasks such as semantic segmentation where images are captured from large field of view (FoV) cameras. State-of-the-art…
Trustworthy Convolutional Neural Networks: A Gradient Penalized-based Approach
Nicholas Halliwell, Freddy Lecue
Convolutional neural networks (CNNs) are commonly used for image classification. Saliency methods are examples of approaches that can be used to interpret CNNs post hoc, identifyin…
Ontology-guided Semantic Composition for Zero-Shot Learning
Jiaoyan Chen, Freddy Lecue, Yuxia Geng +2
Zero-shot learning (ZSL) is a popular research problem that aims at predicting for those classes that have never appeared in the training stage by utilizing the inter-class relatio…
Background Knowledge Injection for Interpretable Sequence Classification
Severin Gsponer, Luca Costabello, Chan Le Van +4
Sequence classification is the supervised learning task of building models that predict class labels of unseen sequences of symbols. Although accuracy is paramount, in certain scen…
Local Score Dependent Model Explanation for Time Dependent Covariates
Xochitl Watts, Freddy Lecue
The use of deep neural networks to make high risk decisions creates a need for global and local explanations so that users and experts have confidence in the modeling algorithms. W…
Augmenting Transfer Learning with Semantic Reasoning
Freddy Lecue, Jiaoyan Chen, Jeff Z. Pan +1
Transfer learning aims at building robust prediction models by transferring knowledge gained from one problem to another. In the semantic Web, learning tasks are enhanced with sema…
Interpretable Credit Application Predictions With Counterfactual Explanations
Rory Mc Grath, Luca Costabello, Chan Le Van +4
We predict credit applications with off-the-shelf, interchangeable black-box classifiers and we explain single predictions with counterfactual explanations. Counterfactual explanat…
Knowledge-based Transfer Learning Explanation
Jiaoyan Chen, Freddy Lecue, Jeff Z. Pan +2
Machine learning explanation can significantly boost machine learning's application in decision making, but the usability of current methods is limited in human-centric explanation…
Semantic Explanations of Predictions
Freddy Lecue, Jiewen Wu
The main objective of explanations is to transmit knowledge to humans. This work proposes to construct informative explanations for predictions made from machine learning models. M…
Learning from Ontology Streams with Semantic Concept Drift
Freddy Lecue, Jiaoyan Chen, Jeff Pan +1
Data stream learning has been largely studied for extracting knowledge structures from continuous and rapid data records. In the semantic Web, data is interpreted in ontologies and…