Meta Clustering of Neural Bandits
Author:
Affiliation:
1. University of Illinois at Urbana-Champaign, Champaign, IL, USA
2. University of Illinois, Urbana-Champaign, Champaign, IL, USA
3. University of Illinois, Urbana Champaign, Champaign, IL, USA
Funder
Agriculture and Food Research Initiative
National Science Foundation
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3637528.3671691
Reference64 articles.
1. Y. Abbasi-Yadkori, D. Pál, and C. Szepesvári. Improved algorithms for linear stochastic bandits. In Advances in Neural Information Processing Systems, pages 2312--2320, 2011.
2. Z. Allen-Zhu, Y. Li, and Z. Song. A convergence theory for deep learning via over-parameterization. In International Conference on Machine Learning, pages 242--252. PMLR, 2019.
3. Generic Outlier Detection in Multi-Armed Bandit
4. Local Clustering in Contextual Multi-Armed Bandits
5. Multi-facet Contextual Bandits
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