From the 1 of 3 linked papers with an AI index.
3 papers
cs.LG2026
From Novice to Expert: Cost-Aware Bandits for Evolving Worker Performance in Crowdsensing
Yin Huang, Qingsong Liu, Jie Xu
The paper proposes a cost‑aware bandit algorithm for mobile crowdsensing that learns how workers' sensing quality improves with experience while accounting for unknown, time‑varyin…
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.LG2024
Learning the Optimal Path and DNN Partition for Collaborative Edge Inference
Yin Huang, Letian Zhang, Jie Xu
Recent advancements in Deep Neural Networks (DNNs) have catalyzed the development of numerous intelligent mobile applications and services. However, they also introduce significant…