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Motivated by high-stakes decision-making domains like personalized medicine where user information is inherently sensitive, we design privacy preserving exploration policies for episodic reinforcement learning (RL).
On the sample complexity of reinforcement learning
Kakade · 2003
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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Reinforcement learning in finite mdps: Pac analysis
Alexander L Strehl, Lihong Li, and Michael L Littman · 2009
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Differential privacy under continual observation
Cynthia Dwork, Moni Naor, Toniann Pitassi, and Guy N Rothblum · 2010
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Near-optimal regret bounds for reinforcement learning
Thomas Jaksch, Ronald Ortner, and Peter Auer · 2010
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Private and continual release of statistics
T-H Hubert Chan, Elaine Shi, and Dawn Song · 2011
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(nearly) optimal algorithms for private online learning in full-information and bandit settings
Abhradeep Guha Thakurta and Adam Smith · 2013
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Rappor: Randomized aggregatable privacy-preserving ordinal response
Úlfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova · 2014
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Mechanism design in large games: incentives and privacy
Michael J. Kearns, Mallesh M. Pai, Aaron Roth, and Jonathan Ullman · 2014
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Sample complexity of episodic fixed-horizon reinforcement learning
Christoph Dann and Emma Brunskill · 2015
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(nearly) optimal differentially private stochastic multi-arm bandits
Nikita Mishra and Abhradeep Thakurta · 2015
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Differentially private policy evaluation
Borja Balle, Maziar Gomrokchi, and Doina Precup · 2016
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Private matchings and allocations
Justin Hsu, Zhiyi Huang, Aaron Roth, Tim Roughgarden, and Zhiwei Steven Wu · 2016
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Algorithms for differentially private multi-armed bandits
Aristide CY Tossou and Christos Dimitrakakis · 2016
Cited alongside, same era.
Minimax regret bounds for reinforcement learning
Mohammad Gheshlaghi Azar, Ian Osband, and Rémi Munos · 2017
Cited alongside, same era.
The price of differential privacy for online learning
Naman Agarwal and Karan Singh · 2017
Cited alongside, same era.
Unifying pac and regret: Uniform pac bounds for episodic reinforcement learning
Christoph Dann, Tor Lattimore, and Emma Brunskill · 2017
Cited alongside, same era.
Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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Differentially private contextual linear bandits
Roshan Shariff and Or Sheffet · 2018
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On the differential privacy of thompson sampling with gaussian prior
Aristide CY Tossou and Christos Dimitrakakis · 2018
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Tensorflow privacy
Galen Andrew, Steve Chien, and Nicolas Papernot · 2019
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Differential privacy for multi-armed bandits: What is it and what is its cost?
Debabrota Basu, Christos Dimitrakakis, and Aristide Tossou · 2019
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Vishesh Karwa and Salil Vadhan · 2017
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Achieving privacy in the adversarial multi-armed bandit
Aristide Charles Yedia Tossou and Christos Dimitrakakis · 2017
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Learning with privacy at scale
Apple Differential Privacy Team · 2017
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The us census bureau adopts differential privacy
John M Abowd · 2018
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Mitigating bias in adaptive data gathering via differential privacy
Seth Neel and Aaron Roth · 2018
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Naoise Holohan, Stefano Braghin, Pól Mac Aonghusa, and Killian Levacher · 2019
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Advances and open problems in federated learning, 2019
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D’Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konečný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, and Sen Zhao · 2019
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Federated learning: Challenges, methods, and future directions, 2019
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith · 2019
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How you act tells a lot: Privacy-leaking attack on deep reinforcement learning
Xinlei Pan, Weiyao Wang, Xiaoshuai Zhang, Bo Li, Jinfeng Yi, and Dawn Song · 2019
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Privacy-preserving q-learning with functional noise in continuous spaces
Baoxiang Wang and Nidhi Hegde · 2019
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Differentially private sql with bounded user contribution, 2019
Royce J Wilson, Celia Yuxin Zhang, William Lam, Damien Desfontaines, Daniel Simmons-Marengo, and Bryant Gipson · 2019
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