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Differentially Private Stochastic Linear Bandits: (Almost) for Free


Jul 07, 2022
Osama A. Hanna, Antonious M. Girgis, Christina Fragouli, Suhas Diggavi

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A Generative Framework for Personalized Learning and Estimation: Theory, Algorithms, and Privacy


Jul 05, 2022
Kaan Ozkara, Antonious M. Girgis, Deepesh Data, Suhas Diggavi

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On Leave-One-Out Conditional Mutual Information For Generalization


Jul 01, 2022
Mohamad Rida Rammal, Alessandro Achille, Aditya Golatkar, Suhas Diggavi, Stefano Soatto

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Decentralized Multi-Task Stochastic Optimization With Compressed Communications


Dec 23, 2021
Navjot Singh, Xuanyu Cao, Suhas Diggavi, Tamer Basar

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* 31 pages, 4 figures 

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Coded Estimation: Design of Backscatter Array Codes for 3D Orientation Estimation


Dec 01, 2021
Mohamad Rida Rammal, Suhas Diggavi, Ashutosh Sabharwal

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QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning


Jul 29, 2021
Kaan Ozkara, Navjot Singh, Deepesh Data, Suhas Diggavi

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* arXiv admin note: substantial text overlap with arXiv:2102.11786 

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Renyi Differential Privacy of the Subsampled Shuffle Model in Distributed Learning


Jul 19, 2021
Antonious M. Girgis, Deepesh Data, Suhas Diggavi

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* arXiv admin note: text overlap with arXiv:2105.05180 

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A Field Guide to Federated Optimization


Jul 14, 2021
Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H. Brendan McMahan, Blaise Aguera y Arcas, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, Suhas Diggavi, Hubert Eichner, Advait Gadhikar, Zachary Garrett, Antonious M. Girgis, Filip Hanzely, Andrew Hard, Chaoyang He, Samuel Horvath, Zhouyuan Huo, Alex Ingerman, Martin Jaggi, Tara Javidi, Peter Kairouz, Satyen Kale, Sai Praneeth Karimireddy, Jakub Konecny, Sanmi Koyejo, Tian Li, Luyang Liu, Mehryar Mohri, Hang Qi, Sashank J. Reddi, Peter Richtarik, Karan Singhal, Virginia Smith, Mahdi Soltanolkotabi, Weikang Song, Ananda Theertha Suresh, Sebastian U. Stich, Ameet Talwalkar, Hongyi Wang, Blake Woodworth, Shanshan Wu, Felix X. Yu, Honglin Yuan, Manzil Zaheer, Mi Zhang, Tong Zhang, Chunxiang Zheng, Chen Zhu, Wennan Zhu

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On the Renyi Differential Privacy of the Shuffle Model


May 11, 2021
Antonious M. Girgis, Deepesh Data, Suhas Diggavi, Ananda Theertha Suresh, Peter Kairouz

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QuPeL: Quantized Personalization with Applications to Federated Learning


Feb 23, 2021
Kaan Ozkara, Navjot Singh, Deepesh Data, Suhas Diggavi

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