3 papers
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…
cs.LG2025
Smoothed Online Optimization for Target Tracking: Robust and Learning-Augmented Algorithms
Ali Zeynali, Mahsa Sahebdel, Qingsong Liu +2
We introduce the Smoothed Online Optimization for Target Tracking (SOOTT) problem, a new framework that integrates three key objectives in online decision-making under uncertainty:…