arXiv Machine Learning By Cristian Palma Foster, Eva Tardos

Learning in Strategic Queuing with Geometric Recharge: From Bandits to Systems

Read the original on arXiv Machine Learning →

arXiv:2610. 19179v1 Announce Type: cross Abstract: We study packet routing in queuing systems where queues act as selfish, myopic agents that use no-regret learning algorithms to select servers with unknown service rates.

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arXiv Machine Learning
Oct 12

Simultaneous Competitive and Regret Guarantees for TCP Acknowledgment

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.

By Tsubasa Harada, Yasushi Kawase, Hanna Sumita
arXiv Machine Learning
Jun 5

Multi-Agent Lipschitz Bandits

arXiv:2602. 16965v2 Announce Type: replace Abstract: We study the decentralized multi-player stochastic bandit problem over a continuous, Lipschitz-structured action space where hard collisions yield zero reward.

By Sourav Chakraborty, Amit Kiran Rege, Claire Monteleoni, Lijun Chen