6 citations · 7 across the 4 of their papers we have counts for
5 papers
The Benefits of Diversity: Combining Comparisons and Ratings for Efficient Scoring
Julien Fageot, Matthias Grossglauser, Lê-Nguyên Hoang +2
Should humans be asked to evaluate entities individually or comparatively? This question has been the subject of long debates. In this work, we show that, interestingly, combining…
Byzantine Machine Learning: MultiKrum and an optimal notion of robustness
Gilles Bareilles, Wassim Bouaziz, Julien Fageot +1
Aggregation rules are the cornerstone of distributed (or federated) learning in the presence of adversaries, under the so-called Byzantine threat model. They are also interesting m…
On Monotonicity in AI Alignment
Gilles Bareilles, Julien Fageot, Lê-Nguyên Hoang +4
Comparison-based preference learning has become central to the alignment of AI models with human preferences. However, these methods may behave counterintuitively. After empiricall…
Generalizing while preserving monotonicity in comparison-based preference learning models
Julien Fageot, Peva Blanchard, Gilles Bareilles +1
If you tell a learning model that you prefer an alternative over another alternative , then you probably expect the model to be monotone, that is, the valuation of incre…
Solidago: A Modular Collaborative Scoring Pipeline
Lê Nguyên Hoang, Romain Beylerian, Bérangère Colbois +6
This paper presents Solidago, an end-to-end modular pipeline to allow any community of users to collaboratively score any number of entities. Solidago proposes a six-module decompo…