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Fatemehsadat Mireshghallah

UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis

Oct 01, 2021
Fatemehsadat Mireshghallah, Vaishnavi Shrivastava, Milad Shokouhi, Taylor Berg-Kirkpatrick, Robert Sim, Dimitrios Dimitriadis

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Style Pooling: Automatic Text Style Obfuscation for Improved Classification Fairness

Sep 10, 2021
Fatemehsadat Mireshghallah, Taylor Berg-Kirkpatrick

* EMNLP 2021 

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Efficient Hyperparameter Optimization for Differentially Private Deep Learning

Aug 09, 2021
Aman Priyanshu, Rakshit Naidu, Fatemehsadat Mireshghallah, Mohammad Malekzadeh

* 4+1 pages, 4 figures, 1 table 

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Benchmarking Differential Privacy and Federated Learning for BERT Models

Jun 26, 2021
Priyam Basu, Tiasa Singha Roy, Rakshit Naidu, Zumrut Muftuoglu, Sahib Singh, Fatemehsadat Mireshghallah

* 4 pages, 3 tables, 1 figure 

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When Differential Privacy Meets Interpretability: A Case Study

Jun 25, 2021
Rakshit Naidu, Aman Priyanshu, Aadith Kumar, Sasikanth Kotti, Haofan Wang, Fatemehsadat Mireshghallah

* 4 pages, 7 figures; Extended abstract presented at RCV-CVPR'21 

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DP-SGD vs PATE: Which Has Less Disparate Impact on Model Accuracy?

Jun 22, 2021
Archit Uniyal, Rakshit Naidu, Sasikanth Kotti, Sahib Singh, Patrik Joslin Kenfack, Fatemehsadat Mireshghallah, Andrew Trask

* 4 pages, 3 images 

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Privacy Regularization: Joint Privacy-Utility Optimization in Language Models

Mar 12, 2021
Fatemehsadat Mireshghallah, Huseyin A. Inan, Marcello Hasegawa, Victor Rühle, Taylor Berg-Kirkpatrick, Robert Sim

* NAACL-HLT 2021 Paper 

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U-Noise: Learnable Noise Masks for Interpretable Image Segmentation

Jan 20, 2021
Teddy Koker, Fatemehsadat Mireshghallah, Tom Titcombe, Georgios Kaissis

* Submitted to ICIP. Revision: corrected affiliation 

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Neither Private Nor Fair: Impact of Data Imbalance on Utility and Fairness in Differential Privacy

Oct 03, 2020
Tom Farrand, Fatemehsadat Mireshghallah, Sahib Singh, Andrew Trask

* 5 pages, 5 figures 

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Privacy in Deep Learning: A Survey

May 09, 2020
Fatemehsadat Mireshghallah, Mohammadkazem Taram, Praneeth Vepakomma, Abhishek Singh, Ramesh Raskar, Hadi Esmaeilzadeh

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A Principled Approach to Learning Stochastic Representations for Privacy in Deep Neural Inference

Mar 26, 2020
Fatemehsadat Mireshghallah, Mohammadkazem Taram, Ali Jalali, Ahmed Taha Elthakeb, Dean Tullsen, Hadi Esmaeilzadeh

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Gradient-Based Deep Quantization of Neural Networks through Sinusoidal Adaptive Regularization

Feb 29, 2020
Ahmed T. Elthakeb, Prannoy Pilligundla, Fatemehsadat Mireshghallah, Tarek Elgindi, Charles-Alban Deledalle, Hadi Esmaeilzadeh

* Preliminary work. Under review 

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Shredder: Learning Noise to Protect Privacy with Partial DNN Inference on the Edge

May 26, 2019
Fatemehsadat Mireshghallah, Mohammadkazem Taram, Prakash Ramrakhyani, Dean Tullsen, Hadi Esmaeilzadeh

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