A Parameter-Free Best-of-Both-Worlds Policy for Queueing Bandits
Jongyeong Lee, Chansoo Kim
Abstract
We study online channel scheduling for a single queue served by K channels under bandit feedback. We propose a simple best-of-both-worlds policy that explores when the queue is empty. During busy periods, it selects the empirical best channel while the prescribed conditions hold and, if any condition is violated, switches to a robust bandit policy to clear the queue. For arbitrary arrivals and services determined by an oblivious adversary, our policy achieves O(K LT) worst-case queue length regret with high probability, where LT is the longest busy-period length. This result improves the previous T3/4 dependence and matches the worst-case lower bound up to logarithmic factors. Under bounded i.i.d. stochastic models with a unique best stabilizing channel, we show that the cumulative queue length regret converges to a finite problem-dependent constant, and further establish the logarithmic expected worst-case queue length regret bound.
Create a lesson
Related papers
Coarse Semantic Blocks for Efficient Training-Free Sparse Attention
Sosuke Kobayashi, Shoichiro Yamaguchi, Seiya Tokui
What Is the Influence of Task Order in Continual Learning? An Empirical Study and Order-Aware Evaluation for Motion Forecasting
Zirui Li, Yunlong Lin, Haohan Yang et al.
Reciprocity Can Erase Direction:tructural Boundaries and Controlled Utility of Magnetic Graph Networks
Dawei Han, Zicheng Wang, Yaoxing Ren et al.
Deep Learning Compilers: Architecture, Optimization, and Empirical Characterization
Jinghao Wen, Ruixuan Wang, Xun Jiao
OmniST: Topology-Invariant Spatiotemporal Forecasting with Guaranteed-Coverage Uncertainty at Unseen Sensor Locations
Praveg Vashishtha
LIRA: A More Interpretable Dynamic Loss-Informed Rank Allocation for Low-Rank Adaptation
Sihao Wang, Bozheng Li, Xuemiao Xu et al.