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cs.LG2024
Contextual Combinatorial Bandits with Probabilistically Triggered Arms
Xutong Liu, Jinhang Zuo, Siwei Wang +4
We study contextual combinatorial bandits with probabilistically triggered arms (CMAB-T) under a variety of smoothness conditions that capture a wide range of applications, suc…
cs.LG2024
Batch-Size Independent Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms or Independent Arms
Xutong Liu, Jinhang Zuo, Siwei Wang +3
In this paper, we study the combinatorial semi-bandits (CMAB) and focus on reducing the dependency of the batch-size in the regret bound, where is the total number of arms…
cs.LG2024
BAFFLE: A Baseline of Backpropagation-Free Federated Learning
Haozhe Feng, Tianyu Pang, Chao Du +3
Federated learning (FL) is a general principle for decentralized clients to train a server model collectively without sharing local data. FL is a promising framework with practical…