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Recent works on neural contextual bandits have achieved compelling performances due to their ability to leverage the strong representation power of neural networks (NNs) for reward prediction.
On the likelihood that one unknown probability exceeds another in view of the evidence of two samples
William R Thompson · 1933
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Using confidence bounds for exploitation-exploration trade-offs
Peter Auer · 2002
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Stochastic linear optimization under bandit feedback
Varsha Dani, Thomas P. Hayes, and Sham M. Kakade · 2008
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Gaussian process optimization in the bandit setting: No regret and experimental design
Niranjan Srinivas, Andreas Krause, Sham M. Kakade, and Matthias Seeger · 2010
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Improved algorithms for linear stochastic bandits
Yasin Abbasi-Yadkori, Dávid Pál, and Csaba Szepesvári · 2011
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Regret analysis of stochastic and nonstochastic multi-armed bandit problems
Sébastien Bubeck and Nicolò Cesa-Bianchi · 2012
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Thompson sampling for contextual bandits with linear payoffs
Shipra Agrawal and Navin Goyal · 2013
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Finite-time analysis of kernelised contextual bandits
Michal Valko, Nathaniel Korda, Rémi Munos, Ilias Flaounas, and Nelo Cristianini · 2013
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Federated learning: Challenges, methods, and future directions
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith · 2014
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On kernelized multi-armed bandits
Sayak Ray Chowdhury and Aditya Gopalan · 2017
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UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
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Action centered contextual bandits
Kristjan Greenewald, Ambuj Tewari, Susan Murphy, and Predag Klasnja · 2017
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Communication-efficient learning of deep networks from decentralized data
H Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, et al · 2017
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Federated deep reinforcement learning
Hankz Hankui Zhuo, Wenfeng Feng, Yufeng Lin, Qian Xu, and Qiang Yang · 2017
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Neural tangent kernel: Convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
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Advances and open problems in federated learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2019
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Federated Bayesian optimization via Thompson sampling
Zhongxiang Dai, Bryan Kian Hsiang Low, and Patrick Jaillet · 2020
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Differentially-private federated linear bandits
Abhimanyu Dubey and Alex Pentland · 2020
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Bandit algorithms
Tor Lattimore and Csaba Szepesvári · 2020
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Federated recommendation system via differential privacy
Tan Li, Linqi Song, and Christina Fragouli · 2020
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Distributed bandit learning: Near-optimal regret with efficient communication
Yuanhao Wang, Jiachen Hu, Xiaoyu Chen, and Liwei Wang · 2020
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Neural contextual bandits with deep representation and shallow exploration
Pan Xu, Zheng Wen, Handong Zhao, and Quanquan Gu · 2020
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Neural contextual bandits with UCB-based exploration
Dongruo Zhou, Lihong Li, and Quanquan Gu · 2020
Federated multi-armed bandits with personalization
Chengshuai Shi, Cong Shen, and Jing Yang · 2021
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Uniform generalization bounds for overparameterized neural networks
Sattar Vakili, Michael Bromberg, Jezabel Garcia, Da-shan Shiu, and Alberto Bernacchia · 2021
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Neural Thompson sampling
Weitong Zhang, Dongruo Zhou, Lihong Li, and Quanquan Gu · 2021
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Flora: Single-shot hyper-parameter optimization for federated learning
Yi Zhou, Parikshit Ram, Theodoros Salonidis, Nathalie Baracaldo, Horst Samulowitz, and Heiko Ludwig · 2021
Later among the works it cites.
Ee-net: Exploitation-exploration neural networks in contextual bandits
Yikun Ban, Yuchen Yan, Arindam Banerjee, and Jingrui He · 2022
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Cited alongside, same era.
Convolutional neural bandit: Provable algorithm for visual-aware advertising
Yikun Ban and Jingrui He · 2021
Cited alongside, same era.
Differentially private federated Bayesian optimization with distributed exploration
Zhongxiang Dai, Bryan Kian Hsiang Low, and Patrick Jaillet · 2021
Cited alongside, same era.
Fault-tolerant federated reinforcement learning with theoretical guarantee
Xiaofeng Fan, Yining Ma, Zhongxiang Dai, Wei Jing, Cheston Tan, and Bryan Kian Hsiang Low · 2021
Cited alongside, same era.
Quanquan Gu, Amin Karbasi, Khashayar Khosravi, Vahab Mirrokni, and Dongruo Zhou · 2021
Cited alongside, same era.
Evaluation of hyperparameter-optimization approaches in an industrial federated learning system
Stephanie Holly, Thomas Hiessl, Safoura Rezapour Lakani, Daniel Schall, Clemens Heitzinger, and Jana Kemnitz · 2021
Cited alongside, same era.
Learning neural contextual bandits through perturbed rewards
Yiling Jia, Weitong Zhang, Dongruo Zhou, Quanquan Gu, and Hongning Wang · 2021
Cited alongside, same era.
SAMBA: A generic framework for secure federated multi-armed bandits
Radu Ciucanu, Pascal Lafourcade, Gael Marcadet, and Marta Soare · 2022
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Sample-then-optimize batch neural Thompson sampling
Zhongxiang Dai, Yao Shu, Bryan Kian Hsiang Low, and Patrick Jaillet · 2022
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Federated multi-armed bandits under byzantine attacks
Ilker Demirel, Yigit Yildirim, and Cem Tekin · 2022
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Byzantine-robust federated linear bandits
Ali Jadbabaie, Haochuan Li, Jian Qian, and Yi Tian · 2022
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Neural contextual bandits without regret
Parnian Kassraie and Andreas Krause · 2022
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Graph neural network bandits
Parnian Kassraie, Andreas Krause, and Ilija Bogunovic · 2022
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Communication efficient distributed learning for kernelized contextual bandits
Chuanhao Li, Huazheng Wang, Mengdi Wang, and Hongning Wang · 2022
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Privacy-preserving communication-efficient federated multi-armed bandits
Tan Li and Linqi Song · 2022
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Offline neural contextual bandits: Pessimism, optimization and generalization
Thanh Nguyen-Tang, Sunil Gupta, A Tuan Nguyen, and Svetha Venkatesh · 2022
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Provably and practically efficient neural contextual bandits
Sudeep Salgia, Sattar Vakili, and Qing Zhao · 2022
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Federated multi-armed bandit via uncoordinated exploration
Zirui Yan, Quan Xiao, Tianyi Chen, and Ali Tajer · 2022
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