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Training Production Language Models without Memorizing User Data

Sep 21, 2020
Swaroop Ramaswamy, Om Thakkar, Rajiv Mathews, Galen Andrew, H. Brendan McMahan, Fran├žoise Beaufays


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Privacy Amplification via Random Check-Ins

Jul 30, 2020
Borja Balle, Peter Kairouz, H. Brendan McMahan, Om Thakkar, Abhradeep Thakurta

* Updated proof for $(\epsilon_0, \delta_0)$-DP local randomizers 

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Understanding Unintended Memorization in Federated Learning

Jun 12, 2020
Om Thakkar, Swaroop Ramaswamy, Rajiv Mathews, Fran├žoise Beaufays


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Characterizing Private Clipped Gradient Descent on Convex Generalized Linear Problems

Jun 11, 2020
Shuang Song, Om Thakkar, Abhradeep Thakurta


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Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis

Jun 21, 2019
Ryan Rogers, Aaron Roth, Adam Smith, Nathan Srebro, Om Thakkar, Blake Woodworth


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Differentially Private Learning with Adaptive Clipping

May 09, 2019
Om Thakkar, Galen Andrew, H. Brendan McMahan


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Differentially Private Matrix Completion Revisited

Jun 12, 2018
Prateek Jain, Om Thakkar, Abhradeep Thakurta

* Updated version. Accepted for presentation at International Conference on Machine Learning (ICML) 2018 

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Model-Agnostic Private Learning via Stability

Mar 14, 2018
Raef Bassily, Om Thakkar, Abhradeep Thakurta


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Max-Information, Differential Privacy, and Post-Selection Hypothesis Testing

Sep 09, 2016
Ryan Rogers, Aaron Roth, Adam Smith, Om Thakkar


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