Showing cs.LGShow all
3 papers · 1 filter
cs.LG2026
Meta-LinEXP3: Online-within-Online Learning for Adversarial Linear Contextual Bandits
Hao Li, Jie Xu, Zheng Xie
Meta-learning has emerged as an effective paradigm for transferring knowledge across sequential bandit tasks. While substantial progress has been made for stochastic bandits and no…
cs.LG2026
From Novice to Expert: Cost-Aware Bandits for Evolving Worker Performance in Crowdsensing
Yin Huang, Qingsong Liu, Jie Xu
Mobile crowdsensing (MC) recruits mobile users to perform sensing tasks using their smartphones, enabling large-scale applications such as traffic monitoring and environmental sens…
cs.LG2026
Online LLM Selection via Constrained Bandits with Time-Varying Demand
Yin Huang, Qingsong Liu, Jie Xu
Large Language Models (LLMs) are increasingly deployed in edge-cloud inference systems to handle diverse user tasks with heterogeneous accuracy, latency, and cost profiles. Selecti…