‪Niloofar (Fatemeh) Mireshghallah‬

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Taylor Berg-KirkpatrickUniversity of California San DiegoEmail verificata su ucsd.edu
Yejin ChoiUniversity of Washington / Allen Institute for Artificial IntelligenceEmail verificata su cs.washington.edu
Kazem TaramAssistant Professor, Purdue UniversityEmail verificata su purdue.edu
Reza ShokriNational University of Singapore; MicrosoftEmail verificata su comp.nus.edu.sg
Dean TullsenDepartment of Computer Science and Engineering, UC San DiegoEmail verificata su ucsd.edu
Amir YazdanbakhshResearch Scientist at Google DeepMindEmail verificata su google.com
Robert SimSr. Principal Research Manager, MicrosoftEmail verificata su microsoft.com
Yulia TsvetkovUniversity of WashingtonEmail verificata su cs.washington.edu
Florian TramèrAssistant Professor of Computer Science, ETH ZurichEmail verificata su inf.ethz.ch
Katherine LeeResearcher, Google DeepMindEmail verificata su google.com
Liwei JiangPhD Student, Paul G. Allen School of Computer Science & Engineering, University of WashingtonEmail verificata su cs.washington.edu
KARTIK GOYALGeorgia Institute of TechnologyEmail verificata su gatech.edu
Huseyin A. InanMicrosoft Research AIEmail verificata su microsoft.com
Andrew TraskUniversity of Oxford and OpenMinedEmail verificata su openmined.org
Anshuman SuriPostdoctoral scholar, Northeastern UniversityEmail verificata su virginia.edu
Tianhao WangUniversity of VirginiaEmail verificata su virginia.edu
Maarten SapCarnegie Mellon UniversityEmail verificata su cmu.edu
Ramesh RaskarAssociate Professor, MIT Media LabEmail verificata su mit.edu
Praneeth VepakommaMassachusetts Institute of Technology, MBZUAIEmail verificata su mit.edu
David EvansUniversity of Virginia, Computer ScienceEmail verificata su virginia.edu

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Niloofar (Fatemeh) Mireshghallah

Altri nomiFatemeh Mireshghallah

Postdoctoral scholar, University of Washington

Email verificata su cs.washington.edu - Home page

Privacy-Preserving Machine Learning Natural Language Processing


Titolo Ordina per citazioni Ordina per anno Ordina per titolo	Citata da Citata da	Anno
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	220	2022
Privacy in deep learning: A survey F Mireshghallah, M Taram, P Vepakomma, A Singh, R Raskar, ... arXiv preprint arXiv:2004.12254, 2020	206*	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	134	2022
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	122*	2020
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	117	2023
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	107*	2018
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	105	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	98*	2022
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	70	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	69	2022
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	66*	2024
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	59*	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	55*	2022
A roadmap to pluralistic alignment T Sorensen, J Moore, J Fisher, M Gordon, N Mireshghallah, CM Rytting, ... arXiv preprint arXiv:2402.05070, 2024	51	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	49	2023
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	46*	2024
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
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	39	2021
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	37	2023
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	34	2021

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