2024/03/19 by Joe Suk, Suk, Joe, Arpit Agarwal +1 · 2 citations
Business, Management and Accounting · Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Distributed Sensor Networks and Detection Algorithms #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Supply Chain and Inventory Management
paper · pdf · doi:10.48550/arxiv.2403.12950
openalex publication_date 2024/03/19 · openalex created_date 2024/03/21 · openalex updated_date 2026/07/28
In K-armed dueling bandits, the learner receives preference feedback between arms, and the regret of an arm is defined in terms of its suboptimality to a winner arm. The non-stationary variant of the problem, motivated by concerns of changing user preferences, has received recent interest (Saha and Gupta, 2022; Buening and Saha, 2023; Suk and Agarwal, 2023). The goal here is to design algorithms with low \em dynamic regret, ideally without foreknowledge of the amount of change. The notion of regret here is tied to a notion of winner arm, most typically taken to be a so-called Condorcet winner or a Borda winner. However, the aforementioned results mostly focus on the Condorcet winner. In comparison, the Borda version of this problem has received less attention which is the focus of this work. We establish the first optimal and adaptive dynamic regret upper bound O(L1/3 K1/3 T2/3 ), where L is the unknown number of significant Borda winner switches. We also introduce a novel weighted Borda score framework which generalizes both the Borda and Condorcet problems. This framework surprisingly allows a Borda-style regret analysis of the Condorcet problem and establishes improved bounds over the theoretical state-of-art in regimes with a large number of arms or many spurious changes in Condorcet winner. Such a generalization was not known and could be of independent interest.