Handling Varied Objectives by Online Decision Making-Reference-Cited by-同舟云学术

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Handling Varied Objectives by Online Decision Making

Published:2024-08-24 Issue: Volume:6 Page:2130-2140
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Ma Lanjihong¹^ORCID,Zhang Zhen-Yu²^ORCID,Ding Yao-Xiang³^ORCID,Zhou Zhi-Hua¹^ORCID

Affiliation:

1. National Key Laboratory for Novel Software Technology, School of Artificial Intelligence, Nanjing University, Nanjing, China

2. Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan

3. State Key Lab of CAD & CG, Zhejiang University, Hangzhou, China

Funder

National Science Foundation of China

Collaborative Innovation Center of Novel Software Technology and Industrialization

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671812

Reference48 articles.

1. Yasin Abbasi-Yadkori, Dávid Pál, and Csaba Szepesvári. Improved algorithms for linear stochastic bandits. In Advances in Neural Information Processing Systems 24 (NIPS), 2312--2320, 2011.

2. Naoki Abe and Philip M. Long. Associative reinforcement learning using linear probabilistic concepts. In Proceedings of the 16th International Conference on Machine Learning (ICML), 3--11, 1999.

3. Amit Agarwal and Elad Hazan. Efficient algorithms for online game playing and universal portfolio management. Electronic Colloquium on Computational Complexity, TR06-033, 2006.

4. The Nonstochastic Multiarmed Bandit Problem

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