# Technical Program

## Paper Detail

 Paper ID MLSP-22.4 Paper Title NEAR-OPTIMAL ALGORITHMS FOR PIECEWISE-STATIONARY CASCADING BANDITS Authors Lingda Wang, Huozhi Zhou, University of Illinois at Urbana-Champaign, United States; Bingcong Li, University of Minnesota - Twin Cities, United States; Lav R. Varshney, Zhizhen Zhao, University of Illinois at Urbana-Champaign, United States Session MLSP-22: Sequential Learning Location Gather.Town Session Time: Wednesday, 09 June, 15:30 - 16:15 Presentation Time: Wednesday, 09 June, 15:30 - 16:15 Presentation Poster Topic Machine Learning for Signal Processing: [MLR-SLER] Sequential learning; sequential decision methods IEEE Xplore Open Preview Click here to view in IEEE Xplore Virtual Presentation Click here to watch in the Virtual Conference Abstract Cascading bandit (CB) is a popular model for web search and online advertising. However, the stationary CB model may be too simple to cope with real-world problems, where user preferences may change over time. Considering piecewise-stationary environments, two efficient algorithms, \texttt{GLRT-Ca}-\texttt{scadeUCB} and \texttt{GLRT-CascadeKL-UCB}, are developed. Comparing with existing works, the proposed algorithms: i) are free of change-point-dependent information for choosing parameters; ii) have fewer tuning parameters; iii) improve regret upper bounds. We also show that the proposed algorithms are optimal up to logarithm terms by deriving a minimax lower bound $\Omega(\sqrt{NLT})$ for piecewise-stationary CB. The efficiency of the proposed algorithms is validated through numerical tests on a real-world benchmark dataset.