Fetching the paper…
Reading the bibliography…
We study locally differentially private (LDP) bandits learning in this paper.
Online convex optimization in the bandit setting: gradient descent without a gradient
A. D. Flaxman, A. T. Kalai, and H. B. McMahan · 2005
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis
C. Dwork, F. McSherry, K. Nissim, and A. Smith · 2006
Earlier work this paper cites.
Logarithmic regret algorithms for online convex optimization
E. Hazan, A. Agarwal, and S. Kale · 2007
Earlier work this paper cites.
Optimal algorithms for online convex optimization with multi-point bandit feedback
A. Agarwal, O. Dekel, and L. Xiao · 2010
Earlier work this paper cites.
Differential privacy under continual observation
C. Dwork, M. Naor, T. Pitassi, and G. N. Rothblum · 2010
Earlier work this paper cites.
Parametric bandits: The generalized linear case
S. Filippi, O. Cappe, A. Garivier, and C. Szepesvári · 2010
Earlier work this paper cites.
A contextual-bandit approach to personalized news article recommendation
L. Li, W. Chu, J. Langford, and R. E. Schapire · 2010
Earlier work this paper cites.
Improved algorithms for linear stochastic bandits
Y. Abbasi-Yadkori, D. Pál, and C. Szepesvári · 2011
Earlier work this paper cites.
Stochastic convex optimization with bandit feedback
A. Agarwal, D. P. Foster, D. J. Hsu, S. M. Kakade, and A. Rakhlin · 2011
Earlier work this paper cites.
The kl-ucb algorithm for bounded stochastic bandits and beyond
A. Garivier and O. Cappé · 2011
Earlier work this paper cites.
What can we learn privately?
S. P. Kasiviswanathan, H. K. Lee, K. Nissim, S. Raskhodnikova, and A. Smith · 2011
Earlier work this paper cites.
Improved regret guarantees for online smooth convex optimization with bandit feedback
A. Saha and A. Tewari · 2011
Earlier work this paper cites.
Online-to-confidence-set conversions and application to sparse stochastic bandits
Y. Abbasi-Yadkori, D. Pal, and C. Szepesvari · 2012
Earlier work this paper cites.
Online bandit learning against an adaptive adversary: from regret to policy regret
R. Arora, O. Dekel, and A. Tewari · 2012
Earlier work this paper cites.
Regret analysis of stochastic and nonstochastic multi-armed bandit problems
S. Bubeck, N. Cesa-Bianchi, et al · 2012
Earlier work this paper cites.
Differentially private online learning
P. Jain, P. Kothari, and A. Thakurta · 2012
Cited alongside, same era.
Local privacy and minimax bounds: Sharp rates for probability estimation
J. Duchi, M. J. Wainwright, and M. I. Jordan · 2013
Cited alongside, same era.
On the complexity of bandit and derivative-free stochastic convex optimization
O. Shamir · 2013
Cited alongside, same era.
(nearly) optimal algorithms for private online learning in full-information and bandit settings
A. G. Thakurta and A. Smith · 2013
Cited alongside, same era.
The algorithmic foundations of differential privacy
C. Dwork and A. Roth · 2014
Cited alongside, same era.
Bandit convex optimization: Towards tight bounds
E. Hazan and K. Levy · 2014
Cited alongside, same era.
Provably optimal algorithms for generalized linear contextual bandits
L. Li, Y. Lu, and D. Zhou · 2017
Later among the works it cites.
Is interaction necessary for distributed private learning?
A. Smith, A. Thakurta, and J. Upadhyay · 2017
Later among the works it cites.
Achieving privacy in the adversarial multi-armed bandit
A. C. Y. Tossou and C. Dimitrakakis · 2017
Later among the works it cites.
Collect at once, use effectively: Making non-interactive locally private learning possible
K. Zheng, W. Mou, and L. Wang · 2017
Later among the works it cites.
Privacy at scale: Local differential privacy in practice
G. Cormode, S. Jha, T. Kulkarni, N. Li, D. Srivastava, and T. Wang · 2018
Later among the works it cites.
Combinatorial pure exploration with continuous and separable reward functions and its applications
W. Huang, J. Ok, L. Li, and W. Chen · 2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Best-arm identification algorithms for multi-armed bandits in the fixed confidence setting
K. Jamieson and R. Nowak · 2014
Cited alongside, same era.
lil’ucb: An optimal exploration algorithm for multi-armed bandits
K. Jamieson, M. Malloy, R. Nowak, and S. Bubeck · 2014
Cited alongside, same era.
(nearly) optimal differentially private stochastic multi-arm bandits
N. Mishra and A. Thakurta · 2015
Cited alongside, same era.
Introduction to online convex optimization
E. Hazan et al · 2016
Cited alongside, same era.
On the complexity of best-arm identification in multi-armed bandit models
E. Kaufmann, O. Cappé, and A. Garivier · 2016
Cited alongside, same era.
Algorithms for differentially private multi-armed bandits
A. C. Tossou and C. Dimitrakakis · 2016
Cited alongside, same era.
Later among the works it cites.
Bandit algorithms
T. Lattimore and C. Szepesvári · 2018
Later among the works it cites.
Differentially private contextual linear bandits
R. Shariff and O. Sheffet · 2018
Later among the works it cites.
High-dimensional probability: An introduction with applications in data science , volume 47
R. Vershynin · 2018
Later among the works it cites.
Empirical risk minimization in non-interactive local differential privacy revisited
D. Wang, M. Gaboardi, and J. Xu · 2018
Later among the works it cites.
Differential privacy for multi-armed bandits: What is it and what is its cost?
D. Basu, C. Dimitrakakis, and A. Tossou · 2019
Later among the works it cites.
An optimal private stochastic-mab algorithm based on optimal private stopping rule
T. Sajed and O. Sheffet · 2019
Later among the works it cites.
An optimal algorithm for stochastic and adversarial bandits
J. Zimmert and Y. Seldin · 2019
Later among the works it cites.
Locally differentially private (contextual) bandits learning
K. Zheng, T. Cai, W. Huang, Z. Li, and L. Wang · 2020
Closest in time.