3 citations · 5 across the 12 of their papers we have counts for
4 papers · 1 filter
Fairness-informed Pareto Optimization : An Efficient Bilevel Framework
Sofiane Tanji, Samuel Vaiter, Yassine Laguel
Despite their promise, fair machine learning methods often yield Pareto-inefficient models, in which the performance of certain groups can be improved without degrading that of oth…
From Shortcut to Induction Head: How Data Diversity Shapes Algorithm Selection in Transformers
Ryotaro Kawata, Yujin Song, Alberto Bietti +4
Transformers can implement both generalizable algorithms (e.g., induction heads) and simple positional shortcuts (e.g., memorizing fixed output positions). In this work, we study h…
Differentiable Generalized Sliced Wasserstein Plans
Laetitia Chapel, Romain Tavenard, Samuel Vaiter
Optimal Transport (OT) has attracted significant interest in the machine learning community, not only for its ability to define meaningful distances between probability distributio…
Learning Theory for Kernel Bilevel Optimization
Fares El Khoury, Edouard Pauwels, Samuel Vaiter +1
Bilevel optimization has emerged as a technique for addressing a wide range of machine learning problems that involve an outer objective implicitly determined by the minimizer of a…