10 papers
When Mobile Crowdsourcing Meets Queueing Systems: Human-in-the-Loop Learning
Hongbo Li, Lingjie Duan, Ness B. Shroff
In service systems, customers now rely on congestion information before deciding which queue or server to join, from restaurants and theme-park attractions to road networks. We stu…
Provable In-Context Learning of Nonlinear Regression with Transformers
Hongbo Li, Lingjie Duan, Yingbin Liang
The transformer architecture, which processes sequences of input tokens to produce outputs for query tokens, has revolutionized numerous areas of machine learning. A defining featu…
To Theoretically Understand Transformer-Based In-Context Learning for Optimizing CSMA
Shugang Hao, Hongbo Li, Lingjie Duan
The binary exponential backoff scheme is widely used in WiFi 7 and still incurs poor throughput performance under dynamic channel environments. Recent model-based approaches (e.g.,…
Competitive Multi-armed Bandit Games for Resource Sharing
Hongbo Li, Lingjie Duan
In modern resource-sharing systems, multiple agents access limited resources with unknown stochastic conditions to perform tasks. When multiple agents access the same resource (arm…
Theory of Mixture-of-Experts for Mobile Edge Computing
Hongbo Li, Lingjie Duan
In mobile edge computing (MEC) networks, mobile users generate diverse machine learning tasks dynamically over time. These tasks are typically offloaded to the nearest available ed…
Theory on Mixture-of-Experts in Continual Learning
Hongbo Li, Sen Lin, Lingjie Duan +2
Continual learning (CL) has garnered significant attention because of its ability to adapt to new tasks that arrive over time. Catastrophic forgetting (of old tasks) has been ident…