arXiv Machine Learning By Tsubasa Harada, Yasushi Kawase, Hanna Sumita

Simultaneous Competitive and Regret Guarantees for TCP Acknowledgment

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The paper investigates TCP acknowledgment strategies that aim to minimize both acknowledgment and waiting costs. It establishes that no deterministic online algorithm can achieve a competitive ratio better than 2, and shows that achieving sublinear regret requires at least $k$ randomized online experts.

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arXiv Machine Learning
Jun 15

Online Convex Optimization with Sublinear Noisy Probes

arXiv:2606. 14640v1 Announce Type: new Abstract: We study Online Convex Optimization (OCO) over a convex set $K\subseteq \mathbb R^d$, where in each round $t$ the learner selects $x_t\in K$ and then observes a convex loss $f_t:K\to[0,1]$, with the goal of minimizing regret to the best fixed decision in hindsight.

By Simone Di Gregorio, Anupam Gupta, Stefano Leonardi, Matteo Russo