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Kernel-based bandit is an extensively studied black-box optimization problem, in which the objective function is assumed to live in a known reproducing kernel Hilbert space.
Gaussian process optimization in the bandit setting: no regret and experimental design
Niranjan Srinivas, Andreas Krause, Sham Kakade, and Matthias Seeger · 2010
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Convergence rates of efficient global optimization algorithms
Adam D Bull · 2011
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Practical bayesian optimization of machine learning algorithms
Jasper Snoek, Hugo Larochelle, and Ryan P Adams · 2012
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Finite-time analysis of kernelised contextual bandits
Michal Valko, Nathan Korda, Rémi Munos, Ilias Flaounas, and Nello Cristianini · 2013
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On explore-then-commit strategies
Aurélien Garivier, Tor Lattimore, and Emilie Kaufmann · 2016
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Taking the human out of the loop: A review of Bayesian optimization
B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, and N. de Freitas · 2016
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On kernelized multi-armed bandits
Sayak Ray Chowdhury and Aditya Gopalan · 2017
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Lower bounds on regret for noisy Gaussian process bandit optimization
Jonathan Scarlett, Ilija Bogunovic, and Volkan Cevher · 2017
Cited alongside, same era.
Bayesian optimization in AlphaGo
Yutian Chen, Aja Huang, Ziyu Wang, Ioannis Antonoglou, Julian Schrittwieser, David Silver, and Nando de Freitas · 2018
Cited alongside, same era.
Neural tangent kernel: Convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
Cited alongside, same era.
Convergence of gaussian process regression with estimated hyper-parameters and applications in bayesian inverse problems
Aretha L. Teckentrup · 2018
Cited alongside, same era.
Gaussian process optimization with adaptive sketching: Scalable and no regret
Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, and Lorenzo Rosasco · 2019
High-dimensional experimental design and kernel bandits
Romain Camilleri, Kevin Jamieson, and Julian Katz-Samuels · 2021
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A domain-shrinking based Bayesian optimization algorithm with order-optimal regret performance
Sudeep Salgia, Sattar Vakili, and Qing Zhao · 2021
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Regret bounds for expected improvement algorithms in gaussian process bandit optimization
Sunil Gupta, Santu Rana, Svetha Venkatesh, et al · 2022
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Neural contextual bandits without regret
Parnian Kassraie and Andreas Krause · 2022
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Gaussian process bandit optimization with few batches
Zihan Li and Jonathan Scarlett · 2022
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Improved convergence rates for sparse approximation methods in kernel-based learning
Sattar Vakili, Jonathan Scarlett, Da-shan Shiu, and Alberto Bernacchia · 2022
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Cited alongside, same era.
Efficient batch black-box optimization with deterministic regret bounds
Yueming Lyu, Yuan Yuan, and Ivor W Tsang · 2019
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Uniform generalization bounds for overparameterized neural networks
Sattar Vakili, Michael Bromberg, Jezabel Garcia, Da-shan Shiu, and Alberto Bernacchia
Cited in the paper.
On information gain and regret bounds in Gaussian process bandits
Sattar Vakili, Kia Khezeli, and Victor Picheny
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Open problem: Tight online confidence intervals for RKHS elements
Sattar Vakili, Jonathan Scarlett, and Tara Javidi
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