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arXiv Machine Learning By Jingxin Zhan, Yuze Han, Zhihua Zhang

Last-Iterate Analyses of FTRL with the 1/2-Tsallis Entropy in Stochastic Bandits

Read the original on arXiv Machine Learning →

arXiv:2510. 22819v3 Announce Type: replace Abstract: The convergence analysis of online learning algorithms is central to machine learning theory, where the last-iterate convergence is particularly important, as it captures the learner's actual decisions and describes the evolution of the learning process over time.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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