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We study privacy-preserving exploration in sequential decision-making for environments that rely on sensitive data such as medical records.
Adaptive estimation of a quadratic functional of a density by model selection
Laurent, B · 2005
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Calibrating noise to sensitivity in private data analysis
Dwork, C., McSherry, F., Nissim, K., and Smith, A · 2006
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Q -learning with linear function approximation
Melo, F. S. and Ribeiro, M. I · 2007
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Differential privacy under continual observation
Dwork, C., Naor, M., Pitassi, T., and Rothblum, G. N · 2010
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Improved algorithms for linear stochastic bandits
Abbasi-Yadkori, Y., Pál, D., and Szepesvári, C · 2011
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Private and continual release of statistics
Chan, T.-H. H., Shi, E., and Song, D · 2011
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Matrix Theory: Basic Results and Techniques
Zhang, F · 2011
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Topics in random matrix theory , volume 132
Tao, T · 2012
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(nearly) optimal algorithms for private online learning in full-information and bandit settings
Guha Thakurta, A. and Smith, A · 2013
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(nearly) optimal differentially private stochastic multi-arm bandits
Mishra, N. and Thakurta, A · 2015
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Concentrated differential privacy: Simplifications, extensions, and lower bounds
Bun, M. and Steinke, T · 2016
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Private matchings and allocations
Hsu, J., Huang, Z., Roth, A., Roughgarden, T., and Wu, Z. S · 2016
Cited alongside, same era.
Generalization and exploration via randomized value functions
Osband, I., Van Roy, B., and Wen, Z · 2016
Cited alongside, same era.
The price of differential privacy for online learning
Agarwal, N. and Singh, K · 2017
Cited alongside, same era.
Provably efficient q-learning with low switching cost
Bai, Y., Xie, T., Jiang, N., and Wang, Y.-X · 2019
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Local differentially private regret minimization in reinforcement learning
Garcelon, E., Perchet, V., Pike-Burke, C., and Pirotta, M · 2020
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Provably efficient reinforcement learning with linear function approximation
Jin, C., Yang, Z., Wang, Z., and Jordan, M. I · 2020
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Private reinforcement learning with pac and regret guarantees
Vietri, G., Balle, B., Krishnamurthy, A., and Wu, Z. S · 2020
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A provably efficient algorithm for linear markov decision process with low switching cost
Gao, M., Xie, T., Du, S. S., and Yang, L. F · 2021
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Achieving privacy in the adversarial multi-armed bandit
Tossou, A. C. Y. and Dimitrakakis, C · 2017
Cited alongside, same era.
Differentially private contextual linear bandits
Shariff, R. and Sheffet, O · 2018
Cited alongside, same era.
Linear least-squares algorithms for temporal difference learning
Bradtke, S. J. and Barto, A. G
Cited in the paper.
Linear least-squares algorithms for temporal difference learning
Bradtke, S. J. and Barto, A. G
Cited in the paper.
Mechanism design in large games: Incentives and privacy
Kearns, M., Pai, M., Roth, A., and Ullman, J
Cited in the paper.
Mechanism design in large games: Incentives and privacy
Kearns, M., Pai, M. M., Roth, A., and Ullman, J
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Differentially private exploration in reinforcement learning with linear representation
Luyo, P., Garcelon, E., Lazaric, A., and Pirotta, M · 2021
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Wang, T., Zhou, D., and Gu, Q · 2021
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Differentially private reinforcement learning with linear function approximation
Zhou, X · 2022
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