12 citations · 23 across the 15 of their papers we have counts for
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cs.LG2022★ 3 cited
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…
cs.LG2022★ 1 cited
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…