2 citations · 2 across the 6 of their papers we have counts for
8 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…
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…
A Case for Specialisation in Non-Human Entities
El-Mahdi El-Mhamdi, Lê-Nguyên Hoang, Mariame Tighanimine
With the rise of large multi-modal AI models, fuelled by recent interest in large language models (LLMs), the notion of artificial general intelligence (AGI) went from being restri…
On Goodhart's law, with an application to value alignment
El-Mahdi El-Mhamdi, Lê-Nguyên Hoang
``When a measure becomes a target, it ceases to be a good measure'', this adage is known as {\it Goodhart's law}. In this paper, we investigate formally this law and prove that it…
The poison of dimensionality
Lê-Nguyên Hoang
This paper advances the understanding of how the size of a machine learning model affects its vulnerability to poisoning, despite state-of-the-art defenses. Given isotropic random…
Generalized Bradley-Terry Models for Score Estimation from Paired Comparisons
Julien Fageot, Sadegh Farhadkhani, Lê Nguyên Hoang +1
Many applications, e.g. in content recommendation, sports, or recruitment, leverage the comparisons of alternatives to score those alternatives. The classical Bradley-Terry model a…
Removing Algorithmic Discrimination (With Minimal Individual Error)
El Mahdi El Mhamdi, Rachid Guerraoui, Lê Nguyên Hoang +1
We address the problem of correcting group discriminations within a score function, while minimizing the individual error. Each group is described by a probability density function…