Fetching the paper…
Reading the bibliography…
Motivated by the increasing concern about privacy in nowadays data-intensive online learning systems, we consider a black-box optimization in the nonparametric Gaussian process setting with local differential privacy (LDP) guarantee.
Asymptotically efficient adaptive allocation rules
Tze Leung Lai and Herbert Robbins · 1985
Earlier work this paper cites.
Gaussian processes in machine learning
Carl Edward Rasmussen · 2003
Earlier work this paper cites.
Gaussian process optimization in the bandit setting: No regret and experimental design
Niranjan Srinivas, Andreas Krause, Sham M Kakade, and Matthias Seeger · 2009
Earlier work this paper cites.
A contextual-bandit approach to personalized news article recommendation
Lihong Li, Wei Chu, John Langford, and Robert E Schapire · 2010
Earlier work this paper cites.
Improved algorithms for linear stochastic bandits
Yasin Abbasi-Yadkori, Dávid Pál, and Csaba Szepesvári · 2011
Earlier work this paper cites.
What can we learn privately?
Shiva Prasad Kasiviswanathan, Homin K Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2011
Earlier work this paper cites.
Bandits with heavy tail
Sébastien Bubeck, Nicolo Cesa-Bianchi, and Gábor Lugosi · 2013
Earlier work this paper cites.
Local privacy and statistical minimax rates
John C Duchi, Michael I Jordan, and Martin J Wainwright · 2013
Earlier work this paper cites.
The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
Earlier work this paper cites.
Differentially private bayesian optimization
Matt Kusner, Jacob Gardner, Roman Garnett, and Kilian Weinberger · 2015
Cited alongside, same era.
Taking the human out of the loop: A review of bayesian optimization
Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P Adams, and Nando De Freitas · 2015
Cited alongside, same era.
On kernelized multi-armed bandits
Sayak Ray Chowdhury and Aditya Gopalan · 2017
Cited alongside, same era.
Lower bounds on regret for noisy gaussian process bandit optimization
Jonathan Scarlett, Ilijia Bogunovic, and Volkan Cevher · 2017
Cited alongside, same era.
Privacy at scale: Local differential privacy in practice
Graham Cormode, Somesh Jha, Tejas Kulkarni, Ninghui Li, Divesh Srivastava, and Tianhao Wang · 2018
Cited alongside, same era.
Almost optimal algorithms for linear stochastic bandits with heavy-tailed payoffs
Han Shao, Xiaotian Yu, Irwin King, and Michael R Lyu · 2018
Later among the works it cites.
Differential privacy for multi-armed bandits: What is it and what is its cost?
Debabrota Basu, Christos Dimitrakakis, and Aristide Tossou · 2019
Later among the works it cites.
Gaussian process optimization with adaptive sketching: Scalable and no regret
Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, and Lorenzo Rosasco · 2019
Later among the works it cites.
Bayesian optimization under heavy-tailed payoffs
Sayak Ray Chowdhury and Aditya Gopalan · 2019
Later among the works it cites.
Mariia Vladimirova, Stéphane Girard, Hien Nguyen, and Julyan Arbel · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Streaming kernel regression with provably adaptive mean, variance, and regularization
Audrey Durand, Odalric-Ambrym Maillard, and Joelle Pineau · 2018
Cited alongside, same era.
Corrupt bandits for preserving local privacy
Pratik Gajane, Tanguy Urvoy, and Emilie Kaufmann · 2018
Cited alongside, same era.
Efficient high dimensional bayesian optimization with additivity and quadrature fourier features
Mojmir Mutny and Andreas Krause · 2018
Cited alongside, same era.
Later among the works it cites.
On lower bounds for standard and robust gaussian process bandit optimization
Xu Cai and Jonathan Scarlett · 2020
Closest in time.
Multi-armed bandits with local differential privacy
Wenbo Ren, Xingyu Zhou, Jia Liu, and Ness B Shroff · 2020
Closest in time.
Locally differentially private (contextual) bandits learning
Kai Zheng, Tianle Cai, Weiran Huang, Zhenguo Li, and Liwei Wang · 2020
Closest in time.