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Contextual bandit algorithms are useful in personalized online decision-making.
Our data, ourselves: Privacy via distributed noise generation
Dwork, C., K. Kenthapadi, F. McSherry, I. Mironov, and M. Naor (2006) · 2006
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Calibrating noise to sensitivity in private data analysis
Dwork, C., F. McSherry, K. Nissim, and A. Smith (2006) · 2006
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Introduction to nonparametric estimation
Tsybakov, A. B. (2008) · 2008
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Differential privacy and robust statistics
Dwork, C. and J. Lei (2009) · 2009
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Learning in a large function space: Privacy-preserving mechanisms for svm learning
Rubinstein, B. I., P. L. Bartlett, L. Huang, and N. Taft (2009) · 2009
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A statistical framework for differential privacy
Wasserman, L. and S. Zhou (2010) · 2010
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Differentially private empirical risk minimization
Chaudhuri, K., C. Monteleoni, and A. D. Sarwate (2011) · 2011
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Scikit-learn: Machine learning in Python
Pedregosa, F., G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay (2011) · 2011
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Making gradient descent optimal for strongly convex stochastic optimization
Rakhlin, A., O. Shamir, and K. Sridharan (2011) · 2011
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Privacy-preserving statistical estimation with optimal convergence rates
Smith, A. (2011) · 2011
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User-friendly tail bounds for matrix martingales
Tropp, J. A. (2011) · 2011
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The algorithmic foundations of differential privacy
Dwork, C. and A. Roth (2013) · 2013
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Rappor: Randomized aggregatable privacy-preserving ordinal response
Erlingsson, Ú., V. Pihur, and A. Korolova (2014) · 2014
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Statistical analysis of stochastic gradient methods for generalized linear models
Toulis, P., E. Airoldi, and J. Rennie (2014) · 2014
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Practical locally private heavy hitters
Bassily, R., K. Nissim, U. Stemmer, and A. Thakurta (2017) · 2017
Cited alongside, same era.
Mostly exploration-free algorithms for contextual bandits
Bastani, H., M. Bayati, and K. Khosravi (2017) · 2017
Cited alongside, same era.
Collecting telemetry data privately
Ding, B., J. Kulkarni, and S. Yekhanin (2017) · 2017
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Scalable generalized linear bandits: Online computation and hashing
Jun, K. S., A. Bhargava, R. Nowak, and R. Willett (2017) · 2017
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Privacy loss in apple’s implementation of differential privacy on macos 10.12
Tang, J., A. Korolova, X. Bai, X. Wang, and X. Wang (2017) · 2017
Cited alongside, same era.
Introduction to multi-armed bandits
Slivkins, A. (2019) · 2019
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High-dimensional statistics: A non-asymptotic viewpoint
Wainwright, M. J. (2019) · 2019
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Personalized dynamic pricing with machine learning: High dimensional features and heterogeneous elasticity
Ban, G.-Y. and N. B. Keskin (2020) · 2020
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Online decision making with high-dimensional covariates
Bastani, H. and M. Bayati (2020) · 2020
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Privacy-preserving dynamic personalized pricing with demand learning
Chen, X., D. Simchi-Levi, and Y. Wang (2020) · 2020
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(Locally) Differentially Private Combinatorial Semi-Bandits
Chen, X., K. Zheng, Z. Zhou, Y. Yang, W. Chen, and L. Wang (2020) · 2020
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Learning with privacy at scale
Team, D. P. (2017) · 2017
Cited alongside, same era.
A Contextual Bandit Bake-off
Bietti, A., A. Agarwal, and J. Langford (2018) · 2018
Cited alongside, same era.
Hedging the drift: Learning to optimize under non-stationarity
Cheung, W. C., D. Simchi-Levi, and R. Zhu (2018) · 2018
Cited alongside, same era.
Minimax optimal procedures for locally private estimation
Duchi, J. C., M. I. Jordan, and M. J. Wainwright (2018) · 2018
Cited alongside, same era.
The externalities of exploration and how data diversity helps exploitation
Raghavan, M., A. Slivkins, J. W. Vaughan, and Z. S. Wu (2018) · 2018
Cited alongside, same era.
Riquelme, C., G. Tucker, and J. Snoek (2018) · 2018
Cited alongside, same era.
Differentially private contextual linear bandits
Shariff, R. and O. Sheffet (2018) · 2018
Cited alongside, same era.
Sequential batch learning in finite-action linear contextual bandits
Han, Y., Z. Zhou, Z. Zhou, J. Blanchet, P. W. Glynn, and Y. Ye (2020) · 2020
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Bandit algorithms
Lattimore, T. and C. Szepesvári (2020) · 2020
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Multi-Armed Bandits with Local Differential Privacy
Ren, W., X. Zhou, J. Liu, and N. B. Shroff (2020) · 2020
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Dynamic batch learning in high-dimensional sparse linear contextual bandits
Ren, Z. and Z. Zhou (2020) · 2020
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Batched learning in generalized linear contextual bandits with general decision sets
Ren, Z., Z. Zhou, and J. R. Kalagnanam (2020) · 2020
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Locally Differentially Private (Contextual) Bandits Learning
Zheng, K., T. Cai, W. Huang, Z. Li, and L. Wang (2020) · 2020
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An efficient algorithm for generalized linear bandit: Online stochastic gradient descent and thompson sampling
Ding, Q., C.-J. Hsieh, and J. Sharpnack (2021) · 2021
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