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Improved Regret Bounds for Bandits with Expert Advice

Academic Article
Publication Date:
2025
Citation:
Improved Regret Bounds for Bandits with Expert Advice / N. Cesa Bianchi, K. Eldowa, E. Esposito, J. Olkhovskaya. - In: JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH. - ISSN 1943-5037. - 83:(2025 Jul), pp. 6.1-6.14. [10.1613/jair.1.16738]
abstract:
In this research note, we revisit the bandits with expert advice problem. Under a restricted feedback model, we prove a lower bound of order (Formula presented) for the worst-case regret, where K is the number of actions, N > K the number of experts, and T the time horizon. This matches a previously known upper bound of the same order and improves upon the best available lower bound of (Formula presented). For the standard feedback model, we prove a new instance-based upper bound that depends on the agreement between the experts and provides a logarithmic improvement compared to prior results.
IRIS type:
01 - Articolo su periodico
List of contributors:
N. Cesa Bianchi, K. Eldowa, E. Esposito, J. Olkhovskaya
Authors of the University:
CESA BIANCHI NICOLO' ANTONIO ( author )
ESPOSITO EMMANUEL ( author )
Link to information sheet:
https://air.unimi.it/handle/2434/1176015
Full Text:
https://air.unimi.it/retrieve/handle/2434/1176015/3112385/16738_final.pdf
Project:
Algorithms, Games, and Digital Markets (ALGADIMAR)
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