Scalable and Robust Online Learning for AI-powered Networked Systems
Author: 
Xutong LiuAuthors Info & Claims
Xutong LiuAuthors Info & ClaimsPages 39 - 42
Abstract
In today's world of pervasive connectivity and intelligent technologies, modern networked systems-ranging from sprawling data centers to large-scale Internet of Things (IoT) systems-have grown by leaps and bounds, unlocking numerous transformative services, like smart cities, immersive mixed reality, and generative artificial intelligence (AI). Traditional network optimization and resource allocation methods-built around static models-are increasingly unable to keep up with the evolving demands of these largescale environments. AI-driven solutions are emerging as game-changers, enabling networked systems to process massive streams of data in real time and adapt seamlessly to ever-changing demands. For instance, AI-powered softwaredefined networking (SDN) controllers can instantly reroute traffic in data centers to prevent congestion, while reinforcement learning (RL)-based task offloading can intelligently allocate resources in massive IoT systems. These advancements make AI central to the next generation of intelligent, adaptive network ecosystems.
References
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Xutong Liu, Jinhang Zuo, Xiaowei Chen, Wei Chen, John C.S. Lui. Multi-layered Network Exploration via Random Walks: From Offline Optimization to Online Learning. ICML, 2021. (Long Oral)
[2]
Xutong Liu, Jinhang Zuo, Xiaowei Chen, Wei Chen, John C.S. Lui. Batch-size independent regret bounds for combinatorial semi-bandits with probabilistically triggered arms or independent arms. NeurIPS, 2022.
[3]
Xutong Liu, Xiangxiang Dai, Xuchuang Wang, Mohammad Hajiesmaili, John C.S. Lui. Combinatorial Logistic Bandits. SIGMETRICS, 2025. (To appear)
[4]
Xutong Liu, Jinhang Zuo, Siwei Wang, John C.S. Lui, Mohammad Hajiesmaili, Adam Wierman, and Wei Chen. Contextual combinatorial bandits with probabilistically triggered arms. ICML, 2023.
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Xutong Liu, Jinhang Zuo, Hong Xie, Carlee Joe- Wong, and John C.S. Lui. Variance-adaptive algorithm for probabilistic maximum coverage bandits with general feedback. INFOCOM, 2023.
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Xutong Liu, Jinhang Zuo, Junkai Wang, Zhiyong Wang, Yuedong Xu, and John C.S. Lui. Learning Context-Aware ProbabilisticMaximum Coverage Bandits: A Variance-Adaptive Approach. INFOCOM, 2024.
[7]
Xutong Liu, Haoru Zhao, Tong Yu, Shuai Li, John C.S. Lui. Federated Online Clustering of Bandits. UAI, 2022.
[8]
Hantao Yang, Xutong Liu, ZhiyongWang, Hong Xie, John C.S. Lui, Defu Lian, and Enhong Chen. Federated Contextual Cascading Bandits with Asynchronous Communication and Heterogeneous Users. AAAI, 2024.
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Xutong Liu, Siwei Wang, Jinhang Zuo, Han Zhong, Xuchuang Wang, Zhiyong Wang, Shuai Li, Mohammad Hajiesmaili, John C.S. Lui, and Wei Chen. Combinatorial Multivariant Multi-Armed Bandits with Applications to Episodic Reinforcement Learning and Beyond. ICML, 2024.
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Zhiyong Wang, Jize Xie, Xutong Liu, Shuai Li, John C.S. Lui. Online Clustering of Bandits with Misspecified User Models. NeurIPS, 2023.
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Xiangxiang Dai, Zeyu Zhang, Peng Yang, Yuedong Xu, Xutong Liu, John C.S. Lui. AxiomVision: Accuracy- Guaranteed Adaptive Visual Model Selection for Perspective- Aware Video Analytics. ACM MM, 2024.
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Xiangxiang Dai, Jin Li, Xutong Liu, Anqi Yu, John C.S. Lui. Cost-Effective Online Multi-LLM Selection with Versatile Reward Models. arXiv:2405.16587, 2024.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 11 January 2025
Published in SIGMETRICS Volume 52, Issue 3
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