19 citations · 22 across the 9 of their papers we have counts for
4 papers · 1 filter
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…
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…
Robust Online Convex Optimization in the Presence of Outliers
Tim van Erven, Sarah Sachs, Wouter M. Koolen +1
We consider online convex optimization when a number k of data points are outliers that may be corrupted. We model this by introducing the notion of robust regret, which measures t…