5 papers
Spectral-Residual Continuous Greedy for Tensor Sampling
Hao Li, Jie Xu, Zheng Xie
Sampling a multidomain tensor from limited measurements is fundamental in structured linear inverse problems. Kronecker-structured sampling avoids the full high-dimensional sensing…
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…
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…
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…
Detecting Adversarial Spectrum Attacks via Distance to Decision Boundary Statistics
Wenwei Zhao, Xiaowen Li, Shangqing Zhao +3
Machine learning has been adopted for efficient cooperative spectrum sensing. However, it incurs an additional security risk due to attacks leveraging adversarial machine learning…