29 citations · 160 across the 52 of their papers we have counts for
58 papers
Proximal basin hopping: global optimization with guarantees
Guillaume Lauga, Cesare Molinari, Samuel Vaiter
Global optimization is a challenging problem, with plenty of algorithms displaying empirical success, but scarce theoretical backing. In this work, we propose a new theoretical fra…
On the Hardness of Junking LLMs
Marco Rando, Samuel Vaiter
Large language models (LLMs) are known to be vulnerable to jailbreak attacks, which typically rely on carefully designed prompts containing explicit semantic structure. These attac…
Characterizations of inexact proximal operators
Guillaume Lauga, Samuel Vaiter
Proximal operators are now ubiquitous in non-smooth optimization. Since their introduction in the seminal work of Moreau, many papers have shown their effectiveness on a wide varie…
Towards Understanding Steering Strength
Magamed Taimeskhanov, Samuel Vaiter, Damien Garreau
A popular approach to post-training control of large language models (LLMs) is the steering of intermediate latent representations. Namely, identify a well-chosen direction dependi…
ZOBA: An Efficient Single-loop Zeroth-order Bilevel Optimization Algorithm
Marco Rando, Samuel Vaiter
Bilevel optimization problems consist of minimizing a value function whose evaluation depends on the solution of an inner optimization problem. These problems are typically tackled…
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…