Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Javidbakht, Omid"'
Autor:
Talwar, Kunal, Wang, Shan, McMillan, Audra, Jina, Vojta, Feldman, Vitaly, Bansal, Pansy, Basile, Bailey, Cahill, Aine, Chan, Yi Sheng, Chatzidakis, Mike, Chen, Junye, Chick, Oliver, Chitnis, Mona, Ganta, Suman, Goren, Yusuf, Granqvist, Filip, Guo, Kristine, Jacobs, Frederic, Javidbakht, Omid, Liu, Albert, Low, Richard, Mascenik, Dan, Myers, Steve, Park, David, Park, Wonhee, Parsa, Gianni, Pauly, Tommy, Priebe, Christian, Rishi, Rehan, Rothblum, Guy, Scaria, Michael, Song, Linmao, Song, Congzheng, Tarbe, Karl, Vogt, Sebastian, Winstrom, Luke, Zhou, Shundong
We revisit the problem of designing scalable protocols for private statistics and private federated learning when each device holds its private data. Locally differentially private algorithms require little trust but are (provably) limited in their u
Externí odkaz:
http://arxiv.org/abs/2307.15017
Autor:
Chadha, Karan, Chen, Junye, Duchi, John, Feldman, Vitaly, Hashemi, Hanieh, Javidbakht, Omid, McMillan, Audra, Talwar, Kunal
In this work, we study practical heuristics to improve the performance of prefix-tree based algorithms for differentially private heavy hitter detection. Our model assumes each user has multiple data points and the goal is to learn as many of the mos
Externí odkaz:
http://arxiv.org/abs/2307.11749
Autor:
McMillan, Audra, Javidbakht, Omid, Talwar, Kunal, Briggs, Elliot, Chatzidakis, Mike, Chen, Junye, Duchi, John, Feldman, Vitaly, Goren, Yusuf, Hesse, Michael, Jina, Vojta, Katti, Anil, Liu, Albert, Lyford, Cheney, Meyer, Joey, Palmer, Alex, Park, David, Park, Wonhee, Parsa, Gianni, Pelzl, Paul, Rishi, Rehan, Song, Congzheng, Wang, Shan, Zhou, Shundong
Privately learning statistics of events on devices can enable improved user experience. Differentially private algorithms for such problems can benefit significantly from interactivity. We argue that an aggregation protocol can enable an interactive
Externí odkaz:
http://arxiv.org/abs/2211.10082
We study adaptive methods for differentially private convex optimization, proposing and analyzing differentially private variants of a Stochastic Gradient Descent (SGD) algorithm with adaptive stepsizes, as well as the AdaGrad algorithm. We provide u
Externí odkaz:
http://arxiv.org/abs/2106.13756
Autor:
Paulik, Matthias, Seigel, Matt, Mason, Henry, Telaar, Dominic, Kluivers, Joris, van Dalen, Rogier, Lau, Chi Wai, Carlson, Luke, Granqvist, Filip, Vandevelde, Chris, Agarwal, Sudeep, Freudiger, Julien, Byde, Andrew, Bhowmick, Abhishek, Kapoor, Gaurav, Beaumont, Si, Cahill, Áine, Hughes, Dominic, Javidbakht, Omid, Dong, Fei, Rishi, Rehan, Hung, Stanley
We describe the design of our federated task processing system. Originally, the system was created to support two specific federated tasks: evaluation and tuning of on-device ML systems, primarily for the purpose of personalizing these systems. In re
Externí odkaz:
http://arxiv.org/abs/2102.08503
Differential Privacy (DP) provides strong guarantees on the risk of compromising a user's data in statistical learning applications, though these strong protections make learning challenging and may be too stringent for some use cases. To address thi
Externí odkaz:
http://arxiv.org/abs/1912.04042
Publikováno v:
EURASIP Journal on Wireless Communications and Networking, Vol 2011, Iss 1, p 62 (2011)
Abstract This paper is a tutorial review on important issues related to code-division multiple-access (CDMA) systems such as channel capacity, power control, and optimum codes; specifically, we consider optimum overloaded codes that achieve errorless
Externí odkaz:
https://doaj.org/article/de4af10f17794c32b8a82af61661f9e5
Akademický článek
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Akademický článek
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Publikováno v:
2016 Annual Conference on Information Science & Systems (CISS); 2016, p117-122, 6p