arXiv Machine Learning By Arpit Agarwal, Rohan Ghuge, Viswanath Nagarajan, Zhengjia Zhuo

Semi-Bandit Learning for Monotone Stochastic Optimization

Read the original on arXiv Machine Learning →

arXiv:2312. 15427v3 Announce Type: replace Abstract: Stochastic optimization is a widely used approach for optimization under uncertainty, where uncertain input parameters are modeled by random variables.

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arXiv Machine Learning
Aug 3

Parameter-Free Heavy-Tailed Bandits

arXiv:2607. 29460v1 Announce Type: new Abstract: Heavy-tailed distributions arise naturally in sequential decision-making problems such as financial investment, online advertising, and network management, where rare but extreme outcomes can dominate performance.

By Gianmarco Genalti, Alberto Maria Metelli
arXiv Machine Learning
Jul 9

Nonlinear Bandit

arXiv:2607. 07304v1 Announce Type: new Abstract: In this paper we first study the problem of generalized linear bandit (GLB) under heavy-tailed noise.

By Tianshuo Zheng, Ting Wu, Zhi-Hua Zhou, Keqin Liu
arXiv Machine Learning
Aug 18

Sequential Batch Learning in Finite-Action Linear Contextual Bandits

arXiv:2004. 06321v2 Announce Type: replace Abstract: We study the sequential batch learning problem in linear contextual bandits with finite action sets, where the decision maker is constrained to split incoming individuals into (at most) a fixed number of batches and can only observe outcomes for the individuals within a batch at the batch's end.

By Yanjun Han, Zhengqing Zhou, Zihao Hu, Jose Blanchet, Peter W. Glynn, Yinyu Ye, Zhengyuan Zhou