no code implementations • NAACL (PrivateNLP) 2022 • Adel Elmahdy, Huseyin A. Inan, Robert Sim
Recent work has demonstrated the successful extraction of training data from generative language models.
no code implementations • 23 Jun 2023 • Adel Elmahdy, Ahmed Salem
In this work, we propose a new targeted data reconstruction attack called the Mix And Match attack, which takes advantage of the fact that most classification models are based on LLM.
no code implementations • 9 Jun 2022 • Adel Elmahdy, Huseyin A. Inan, Robert Sim
Recent work has demonstrated the successful extraction of training data from generative language models.
no code implementations • NeurIPS 2020 • Adel Elmahdy, Junhyung Ahn, Changho Suh, Soheil Mohajer
We consider a matrix completion problem that exploits social or item similarity graphs as side information.
no code implementations • 12 Sep 2021 • Junhyung Ahn, Adel Elmahdy, Soheil Mohajer, Changho Suh
In the achievability proof, we demonstrate that probability of error of the maximum likelihood estimator vanishes for sufficiently large number of users and items, if all sufficient conditions are satisfied.
no code implementations • 11 Jul 2018 • Adel Elmahdy, Soheil Mohajer
In every shuffling iteration, each worker node processes a new subset of files, and has excess storage to partially cache the remaining files, assuming the cached files are uncoded.
1 code implementation • ICML 2017 • Soheil Mohajer, Changho Suh, Adel Elmahdy
We explore an active top-$K$ ranking problem based on pairwise comparisons that are collected possibly in a sequential manner as per our design choice.