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Learning With Guarantee Via Constrained Multi-Armed Bandit: Theory and Network Applications.

, , , and . IEEE Trans. Mob. Comput., 22 (9): 5346-5358 (September 2023)

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Learning With Guarantee Via Constrained Multi-Armed Bandit: Theory and Network Applications., , , and . IEEE Trans. Mob. Comput., 22 (9): 5346-5358 (September 2023)To Collaborate or Not in Distributed Statistical Estimation with Resource Constraints?, , and . CoRR, (2022)Graphlet Count Estimation via Convolutional Neural Networks., , , and . CoRR, (2018)Exploration for Free: How Does Reward Heterogeneity Improve Regret in Cooperative Multi-agent Bandits?, , , , , , and . UAI, volume 216 of Proceedings of Machine Learning Research, page 2192-2202. PMLR, (2023)Hierarchical Learning Algorithms for Multi-scale Expert Problems., , , , and . SIGMETRICS (Abstracts), page 71-72. ACM, (2022)Cooperative Stochastic Bandits with Asynchronous Agents and Constrained Feedback., , , , , and . NeurIPS, page 8885-8897. (2021)On-Demand Communication for Asynchronous Multi-Agent Bandits., , , , , , and . AISTATS, volume 206 of Proceedings of Machine Learning Research, page 3903-3930. PMLR, (2023)An Online Learning Approach to Network Application Optimization with Guarantee., , , and . INFOCOM, page 2006-2014. IEEE, (2018)Quantum Best Arm Identification., , , , , and . SIGMETRICS Perform. Evaluation Rev., 51 (2): 72-74 (September 2023)Learning to count: A deep learning framework for graphlet count estimation., , , and . Netw. Sci., 9 (S1): S23-S60 (October 2021)