12 citations · 23 across the 15 of their papers we have counts for
15 papers
Shieldstral
Antonia Calvi, Avinash Sooriyarachchi, Giada Pistilli +273
We introduce Shieldstral, a 3B-parameter policy-adaptive multimodal safety classifier that matches or outperforms models nearly 7 its size on text safety benchmarks and set…
Stability and Convergence of Optimistic Exponential Weights with Asymmetric Step Sizes in Bimatrix Games
Hédi Hadiji, Sarah Sachs
We study bimatrix two-player games and investigate the last-iterate convergence and stability of equilibria for the iterates generated by the optimistic exponential weights method.…
Tractable Instances of Bilinear Maximization: Implementing LinUCB on Ellipsoids
Raymond Zhang, Hédi Hadiji, Richard Combes
We consider the maximization of over , with convex and an ellipsoid. This prob…
Linear Bandits on Ellipsoids: Minimax Optimal Algorithms
Raymond Zhang, Hedi Hadiji, Richard Combes
We consider linear stochastic bandits where the set of actions is an ellipsoid. We provide the first known minimax optimal algorithm for this problem. We first derive a novel infor…
An Online Feasible Point Method for Benign Generalized Nash Equilibrium Problems
Sarah Sachs, Hedi Hadiji, Tim van Erven +1
We consider a repeatedly played generalized Nash equilibrium game. This induces a multi-agent online learning problem with joint constraints. An important challenge in this setting…
Tracking solutions of time-varying variational inequalities
Hédi Hadiji, Sarah Sachs, Cristóbal Guzmán
Tracking the solution of time-varying variational inequalities is an important problem with applications in game theory, optimization, and machine learning. Existing work considers…
Towards Characterizing the First-order Query Complexity of Learning (Approximate) Nash Equilibria in Zero-sum Matrix Games
Hédi Hadiji, Sarah Sachs, Tim van Erven +1
In the first-order query model for zero-sum matrix games, players observe the expected pay-offs for all their possible actions under the randomized action played by the…
Accelerated Rates between Stochastic and Adversarial Online Convex Optimization
Sarah Sachs, Hedi Hadiji, Tim van Erven +1
Stochastic and adversarial data are two widely studied settings in online learning. But many optimization tasks are neither i.i.d. nor fully adversarial, which makes it of fundamen…
Between Stochastic and Adversarial Online Convex Optimization: Improved Regret Bounds via Smoothness
Sarah Sachs, Hédi Hadiji, Tim van Erven +1
Stochastic and adversarial data are two widely studied settings in online learning. But many optimization tasks are neither i.i.d. nor fully adversarial, which makes it of fundamen…
Scale-free Unconstrained Online Learning for Curved Losses
Jack J. Mayo, Hédi Hadiji, Tim van Erven
A sequence of works in unconstrained online convex optimisation have investigated the possibility of adapting simultaneously to the norm of the comparator and the maximum norm…
Distributed Online Learning for Joint Regret with Communication Constraints
Dirk van der Hoeven, Hédi Hadiji, Tim van Erven
We consider distributed online learning for joint regret with communication constraints. In this setting, there are multiple agents that are connected in a graph. Each round, an ad…
Diversity-Preserving K-Armed Bandits, Revisited
Hédi Hadiji, Sébastien Gerchinovitz, Jean-Michel Loubes +1
We consider the bandit-based framework for diversity-preserving recommendations introduced by Celis et al. (2019), who approached it in the case of a polytope mainly by a reduction…
Adaptation to the Range in -Armed Bandits
Hédi Hadiji, Gilles Stoltz
We consider stochastic bandit problems with arms, each associated with a bounded distribution supported on the range . We do not assume that the range is known a…
Polynomial Cost of Adaptation for X -Armed Bandits
Hédi Hadiji
In the context of stochastic continuum-armed bandits, we present an algorithm that adapts to the unknown smoothness of the objective function. We exhibit and compute a polynomial c…
KL-UCB-switch: optimal regret bounds for stochastic bandits from both a distribution-dependent and a distribution-free viewpoints
Aurélien Garivier, Hédi Hadiji, Pierre Menard +1
We consider -armed stochastic bandits and consider cumulative regret bounds up to time . We are interested in strategies achieving simultaneously a distribution-free regret b…