Literature Database Entry

tao2025robust

Youming Tao, Xiuzhen Cheng, Falko Dressler, Zhipeng Cai and Dongxiao Yu, "Robust Matroid Bandit Optimization: Near-Optimal Rates under Adversarial Contamination," Theoretical Computer Science, vol. 1051, pp. 115416, October 2025.

Abstract

We study the matroid bandit optimization problem, a fundamental and broadly applicable framework for combinatorial multi-armed bandits where the action space is constrained by a matroid. In particular, we address the challenge of designing algorithms that remain effective under adversarial contamination of feedback rewards, which may severely degrade performance or even mislead existing methods. Our main contribution is an efficient and robust algorithm named ROMM, which builds upon the principle of optimistic matroid maximization and leverages robust statistical estimators to assess base arm quality in polynomial time. Under the ϵ-contamination model, we establish lower bounds and and prove that ROMM achieves near-optimal regret guarantees up to polylogarithmic factors. Our analysis further reveals a sharp phase transition between the low and high contamination regimes. Notably, ROMM can tolerate up to a universal constant fraction of corrupted feedback, which is optimal under mild conditions. Finally, we validate our theoretical findings with numerical experiments that demonstrate the effectiveness of the proposed method.

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Youming Tao
Xiuzhen Cheng
Falko Dressler
Zhipeng Cai
Dongxiao Yu

BibTeX reference

@article{tao2025robust,
    author = {Tao, Youming and Cheng, Xiuzhen and Dressler, Falko and Cai, Zhipeng and Yu, Dongxiao},
    doi = {10.1016/j.tcs.2025.115416},
    title = {{Robust Matroid Bandit Optimization: Near-Optimal Rates under Adversarial Contamination}},
    pages = {115416},
    journal = {Theoretical Computer Science},
    issn = {0304-3975},
    publisher = {Elsevier},
    month = {10},
    volume = {1051},
    year = {2025},
   }
   
   

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Last modified: 2025-07-17