LGMar 3
A Short Note on a Variant of the Squint Algorithm
arXiv:2603.03409v1h-index: 2
Originality Synthesis-oriented
AI Analysis
This is an incremental improvement for researchers in online learning and optimization, focusing on theoretical regret bounds.
The paper tackles the expert problem by proposing a simple variant of the Squint algorithm, proving that it achieves a regret bound similar to a recent variant of the NormalHedge algorithm.
This short note describes a simple variant of the Squint algorithm of Koolen and Van Erven [2015] for the classic expert problem. Via an equally simple modification of their proof, we prove that this variant ensures a regret bound that resembles the one shown in a recent work by Freund et al. [2026] for a variant of the NormalHedge algorithm [Chaudhuri et al., 2009].