‪Niloofar (Fatemeh) Mireshghallah‬

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Taylor Berg-KirkpatrickUniversity of California San DiegoAdresse e-mail validée de ucsd.edu
Yejin ChoiStanford University / NVIDIAAdresse e-mail validée de stanford.edu
Reza ShokriNational University of Singapore; MicrosoftAdresse e-mail validée de comp.nus.edu.sg
Kazem TaramAssistant Professor, Purdue UniversityAdresse e-mail validée de purdue.edu
Amir YazdanbakhshResearch Scientist at Google DeepMindAdresse e-mail validée de google.com
Dean TullsenDepartment of Computer Science and Engineering, UC San DiegoAdresse e-mail validée de ucsd.edu
Robert SimSr. Principal Research Manager, MicrosoftAdresse e-mail validée de microsoft.com
Yulia TsvetkovUniversity of WashingtonAdresse e-mail validée de cs.washington.edu
Florian TramèrAssistant Professor of Computer Science, ETH ZurichAdresse e-mail validée de inf.ethz.ch
Katherine LeeResearcher, Google DeepMindAdresse e-mail validée de google.com
KARTIK GOYALGeorgia Institute of TechnologyAdresse e-mail validée de gatech.edu
Huseyin A. InanMicrosoft Research AIAdresse e-mail validée de microsoft.com
Praneeth VepakommaMassachusetts Institute of Technology, MBZUAIAdresse e-mail validée de mit.edu
Andrew TraskUniversity of Oxford and OpenMinedAdresse e-mail validée de openmined.org
Maarten SapCarnegie Mellon UniversityAdresse e-mail validée de cmu.edu
Tianhao WangUniversity of VirginiaAdresse e-mail validée de virginia.edu
David EvansUniversity of Virginia, Computer ScienceAdresse e-mail validée de virginia.edu
Ramesh RaskarAssociate Professor, MIT Media LabAdresse e-mail validée de mit.edu
Alexander CloningerAssociate Professor, Department of Mathematics & Halicioglu Data Science Institute, UC San DiegoAdresse e-mail validée de ucsd.edu

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Niloofar (Fatemeh) Mireshghallah

Autres nomsFatemeh Mireshghallah

Postdoctoral scholar, University of Washington

Adresse e-mail validée de cs.washington.edu - Page d'accueil

Privacy-Preserving Machine Learning Natural Language Processing


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
What does it mean for a language model to preserve privacy? H Brown, K Lee, F Mireshghallah, R Shokri, F Tramèr Proceedings of the 2022 ACM conference on fairness, accountability, and …, 2022	249	2022
Privacy in deep learning: A survey F Mireshghallah, M Taram, P Vepakomma, A Singh, R Raskar, ... arXiv preprint arXiv:2004.12254, 2020	216*	2020
Quantifying privacy risks of masked language models using membership inference attacks F Mireshghallah, K Goyal, A Uniyal, T Berg-Kirkpatrick, R Shokri arXiv preprint arXiv:2203.03929, 2022	152	2022
Membership inference attacks against language models via neighbourhood comparison J Mattern, F Mireshghallah, Z Jin, B Schölkopf, M Sachan, ... arXiv preprint arXiv:2305.18462, 2023	143	2023
Shredder: Learning noise distributions to protect inference privacy F Mireshghallah, M Taram, P Ramrakhyani, A Jalali, D Tullsen, ... Proceedings of the Twenty-Fifth International Conference on Architectural …, 2020	128*	2020
Neither private nor fair: Impact of data imbalance on utility and fairness in differential privacy T Farrand, F Mireshghallah, S Singh, A Trask Proceedings of the 2020 workshop on privacy-preserving machine learning in …, 2020	117	2020
An empirical analysis of memorization in fine-tuned autoregressive language models F Mireshghallah, A Uniyal, T Wang, DK Evans, T Berg-Kirkpatrick Proceedings of the 2022 Conference on Empirical Methods in Natural Language …, 2022	115*	2022
Releq: A reinforcement learning approach for deep quantization of neural networks AT Elthakeb, P Pilligundla, FS Mireshghallah, A Yazdanbakhsh, ... arXiv preprint arXiv:1811.01704, 2018	111*	2018
Do membership inference attacks work on large language models? M Duan, A Suri, N Mireshghallah, S Min, W Shi, L Zettlemoyer, Y Tsvetkov, ... arXiv preprint arXiv:2402.07841, 2024	93*	2024
Benchmarking differential privacy and federated learning for bert models P Basu, TS Roy, R Naidu, Z Muftuoglu, S Singh, F Mireshghallah arXiv preprint arXiv:2106.13973, 2021	75	2021
Mix and match: Learning-free controllable text generation using energy language models F Mireshghallah, K Goyal, T Berg-Kirkpatrick arXiv preprint arXiv:2203.13299, 2022	71	2022
A roadmap to pluralistic alignment T Sorensen, J Moore, J Fisher, M Gordon, N Mireshghallah, CM Rytting, ... arXiv preprint arXiv:2402.05070, 2024	67	2024
Can llms keep a secret? testing privacy implications of language models via contextual integrity theory N Mireshghallah, H Kim, X Zhou, Y Tsvetkov, M Sap, R Shokri, Y Choi arXiv preprint arXiv:2310.17884, 2023	65	2023
Not all features are equal: Discovering essential features for preserving prediction privacy F Mireshghallah, M Taram, A Jalali, ATT Elthakeb, D Tullsen, ... Proceedings of the Web Conference 2021, 669-680, 2021	64*	2021
Flute: A scalable, extensible framework for high-performance federated learning simulations DD Mirian Hipolito Garcia, Andre Manoel, Daniel Madrigal Diaz, Fatemehsadat ... arXiv preprint arXiv:2203.13789, 2022	58*	2022
Smaller Language Models are Better Zero-shot Machine-Generated Text Detectors N Mireshghallah, J Mattern, S Gao, R Shokri, T Berg-Kirkpatrick Proceedings of the 18th Conference of the European Chapter of the …, 2024	52*	2024
Privacy-preserving in-context learning with differentially private few-shot generation X Tang, R Shin, HA Inan, A Manoel, F Mireshghallah, Z Lin, S Gopi, ... arXiv preprint arXiv:2309.11765, 2023	48	2023
Dp-sgd vs pate: Which has less disparate impact on model accuracy? A Uniyal, R Naidu, S Kotti, S Singh, PJ Kenfack, F Mireshghallah, A Trask arXiv preprint arXiv:2106.12576, 2021	41	2021
Privacy Regularization: Joint Privacy-Utility Optimization in Language Models F Mireshghallah, HA Inan, M Hasegawa, V Rühle, T Berg-Kirkpatrick, ... Proceedings of the 2021 Conference of the North American Chapter of the …, 2021	40	2021
UserIdentifier: implicit user representations for simple and effective personalized sentiment analysis F Mireshghallah, V Shrivastava, M Shokouhi, T Berg-Kirkpatrick, R Sim, ... arXiv preprint arXiv:2110.00135, 2021	36	2021

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