10 citations · 26 across the 9 of their papers we have counts for
9 papers
ELIXIR: Efficient and LIghtweight model for eXplaIning Recommendations
Ben Kabongo, Vincent Guigue, Pirmin Lemberger
Collaborative filtering drives many successful recommender systems but struggles with fine-grained user-item interactions and explainability. As users increasingly seek transparent…
Nonlinear Concept Erasure: a Density Matching Approach
Antoine Saillenfest, Pirmin Lemberger
Ensuring that neural models used in real-world applications cannot infer sensitive information, such as demographic attributes like gender or race, from text representations is a c…
Explaining Text Classifiers with Counterfactual Representations
Pirmin Lemberger, Antoine Saillenfest
One well motivated explanation method for classifiers leverages counterfactuals which are hypothetical events identical to real observations in all aspects except for one feature.…
Towards Scalable Adaptive Learning with Graph Neural Networks and Reinforcement Learning
Jean Vassoyan, Jill-Jênn Vie, Pirmin Lemberger
Adaptive learning is an area of educational technology that consists in delivering personalized learning experiences to address the unique needs of each learner. An important subfi…
How Graph Structure and Label Dependencies Contribute to Node Classification in a Large Network of Documents
Pirmin Lemberger, Antoine Saillenfest
We introduce a new dataset named WikiVitals which contains a large graph of 48k mutually referred Wikipedia articles classified into 32 categories and connected by 2.3M edges. Our…
Reconciling Causality and Statistics
Pirmin Lemberger, Denis Oblin
Statisticians have warned us since the early days of their discipline that experimental correlation between two observations by no means implies the existence of a causal relation.…
Deep Learning Models for Automatic Summarization
Pirmin Lemberger
Text summarization is an NLP task which aims to convert a textual document into a shorter one while keeping as much meaning as possible. This pedagogical article reviews a number o…
A Primer on Domain Adaptation
Pirmin Lemberger, Ivan Panico
Standard supervised machine learning assumes that the distribution of the source samples used to train an algorithm is the same as the one of the target samples on which it is supp…
On Generalization and Regularization in Deep Learning
Pirmin Lemberger
Why do large neural network generalize so well on complex tasks such as image classification or speech recognition? What exactly is the role regularization for them? These are argu…