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O. Javidbakht

6 papers hereh-index 7306 citations15 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author4
  • last author1

Across the 5 of 6 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • cs.CR2

identity via Semantic Scholar / OpenAlex

activity
20192023
most citedPrivate Adaptive Gradient Methods for Convex Optimization

12 citations · 27 across the 5 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2023★ 1 cited

Differentially Private Heavy Hitter Detection using Federated Analytics

Karan Chadha, Junye Chen, John Duchi +5

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 use…

cs.LG2021★ 12 cited

Private Adaptive Gradient Methods for Convex Optimization

Hilal Asi, John Duchi, Alireza Fallah +2

We study adaptive methods for differentially private convex optimization, proposing and analyzing differentially private variants of a Stochastic Gradient Descent (SGD) algorithm w…

cs.LG2021★ 2 cited

Federated Evaluation and Tuning for On-Device Personalization: System Design & Applications

Matthias Paulik, Matt Seigel, Henry Mason +19

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 sy…

cs.LG2019★ 9 cited

Element Level Differential Privacy: The Right Granularity of Privacy

Hilal Asi, John Duchi, Omid Javidbakht

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 c…

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