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ORCIDFatemehsadat Mireshghallah
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2020 – today
- 2023
[c15]Fatemehsadat Mireshghallah, Yu Su, Tatsunori Hashimoto, Jason Eisner, Richard Shin:
Privacy-Preserving Domain Adaptation of Semantic Parsers. ACL (1) 2023: 4950-4970- [c14]Justus Mattern, Fatemehsadat Mireshghallah, Zhijing Jin, Bernhard Schölkopf, Mrinmaya Sachan, Taylor Berg-Kirkpatrick:
Membership Inference Attacks against Language Models via Neighbourhood Comparison. ACL (Findings) 2023: 11330-11343 - [i23]Fatemehsadat Mireshghallah, Justus Mattern, Sicun Gao, Reza Shokri, Taylor Berg-Kirkpatrick:
Smaller Language Models are Better Black-box Machine-Generated Text Detectors. CoRR abs/2305.09859 (2023) - [i22]Aman Priyanshu, Supriti Vijay, Ayush Kumar, Rakshit Naidu, Fatemehsadat Mireshghallah:
Are Chatbots Ready for Privacy-Sensitive Applications? An Investigation into Input Regurgitation and Prompt-Induced Sanitization. CoRR abs/2305.15008 (2023) - [i21]Justus Mattern, Fatemehsadat Mireshghallah, Zhijing Jin, Bernhard Schölkopf, Mrinmaya Sachan, Taylor Berg-Kirkpatrick:
Membership Inference Attacks against Language Models via Neighbourhood Comparison. CoRR abs/2305.18462 (2023) - 2022
- [c13]Fatemehsadat Mireshghallah, Kartik Goyal, Taylor Berg-Kirkpatrick:
Mix and Match: Learning-free Controllable Text Generationusing Energy Language Models. ACL (1) 2022: 401-415 - [c12]Fatemehsadat Mireshghallah, Archit Uniyal, Tianhao Wang, David Evans, Taylor Berg-Kirkpatrick:
An Empirical Analysis of Memorization in Fine-tuned Autoregressive Language Models. EMNLP 2022: 1816-1826 - [c11]Fatemehsadat Mireshghallah, Kartik Goyal, Archit Uniyal, Taylor Berg-Kirkpatrick, Reza Shokri:
Quantifying Privacy Risks of Masked Language Models Using Membership Inference Attacks. EMNLP 2022: 8332-8347 - [c10]Hannah Brown
, Katherine Lee, Fatemehsadat Mireshghallah, Reza Shokri, Florian Tramèr:
What Does it Mean for a Language Model to Preserve Privacy? FAccT 2022: 2280-2292 - [c9]Fatemehsadat Mireshghallah, Vaishnavi Shrivastava, Milad Shokouhi, Taylor Berg-Kirkpatrick, Robert Sim, Dimitrios Dimitriadis:
UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis. NAACL-HLT 2022: 3449-3456 - [c8]Fatemehsadat Mireshghallah, Arturs Backurs, Huseyin A. Inan, Lukas Wutschitz, Janardhan Kulkarni:
Differentially Private Model Compression. NeurIPS 2022 - [i20]Hannah Brown, Katherine Lee, Fatemehsadat Mireshghallah, Reza Shokri, Florian Tramèr:
What Does it Mean for a Language Model to Preserve Privacy? CoRR abs/2202.05520 (2022) - [i19]Fatemehsadat Mireshghallah, Kartik Goyal, Archit Uniyal, Taylor Berg-Kirkpatrick, Reza Shokri:
Quantifying Privacy Risks of Masked Language Models Using Membership Inference Attacks. CoRR abs/2203.03929 (2022) - [i18]Fatemehsadat Mireshghallah, Kartik Goyal, Taylor Berg-Kirkpatrick:
Mix and Match: Learning-free Controllable Text Generation using Energy Language Models. CoRR abs/2203.13299 (2022) - [i17]Fatemehsadat Mireshghallah, Archit Uniyal, Tianhao Wang, David Evans, Taylor Berg-Kirkpatrick:
Memorization in NLP Fine-tuning Methods. CoRR abs/2205.12506 (2022) - [i16]Fatemehsadat Mireshghallah, Arturs Backurs, Huseyin A. Inan, Lukas Wutschitz, Janardhan Kulkarni:
Differentially Private Model Compression. CoRR abs/2206.01838 (2022) - [i15]Fatemehsadat Mireshghallah, Nikolai Vogler, Junxian He, Omar Florez, Ahmed El-Kishky, Taylor Berg-Kirkpatrick:
Non-Parametric Temporal Adaptation for Social Media Topic Classification. CoRR abs/2209.05706 (2022) - [i14]Fatemehsadat Mireshghallah, Richard Shin, Yu Su, Tatsunori Hashimoto, Jason Eisner:
Privacy-Preserving Domain Adaptation of Semantic Parsers. CoRR abs/2212.10520 (2022) - 2021
- [c7]Fatemehsadat Mireshghallah, Taylor Berg-Kirkpatrick:
Style Pooling: Automatic Text Style Obfuscation for Improved Classification Fairness. EMNLP (1) 2021: 2009-2022 - [c6]Teddy Koker, Fatemehsadat Mireshghallah, Tom Titcombe, Georgios Kaissis:
U-Noise: Learnable Noise Masks for Interpretable Image Segmentation. ICIP 2021: 394-398 - [c5]Fatemehsadat Mireshghallah, Huseyin A. Inan, Marcello Hasegawa, Victor Rühle, Taylor Berg-Kirkpatrick, Robert Sim:
Privacy Regularization: Joint Privacy-Utility Optimization in LanguageModels. NAACL-HLT 2021: 3799-3807 - [c4]Fatemehsadat Mireshghallah, Mohammadkazem Taram, Ali Jalali, Ahmed Taha Elthakeb, Dean M. Tullsen, Hadi Esmaeilzadeh:
Not All Features Are Equal: Discovering Essential Features for Preserving Prediction Privacy. WWW 2021: 669-680 - [i13]Teddy Koker, Fatemehsadat Mireshghallah, Tom Titcombe, Georgios Kaissis:
U-Noise: Learnable Noise Masks for Interpretable Image Segmentation. CoRR abs/2101.05791 (2021) - [i12]Fatemehsadat Mireshghallah, Huseyin A. Inan, Marcello Hasegawa, Victor Rühle, Taylor Berg-Kirkpatrick, Robert Sim:
Privacy Regularization: Joint Privacy-Utility Optimization in Language Models. CoRR abs/2103.07567 (2021) - [i11]Archit Uniyal, Rakshit Naidu, Sasikanth Kotti, Sahib Singh, Patrik Joslin Kenfack, Fatemehsadat Mireshghallah, Andrew Trask:
DP-SGD vs PATE: Which Has Less Disparate Impact on Model Accuracy? CoRR abs/2106.12576 (2021) - [i10]Rakshit Naidu, Aman Priyanshu, Aadith Kumar, Sasikanth Kotti, Haofan Wang, Fatemehsadat Mireshghallah:
When Differential Privacy Meets Interpretability: A Case Study. CoRR abs/2106.13203 (2021) - [i9]Priyam Basu, Tiasa Singha Roy, Rakshit Naidu, Zümrüt Müftüoglu, Sahib Singh, Fatemehsadat Mireshghallah:
Benchmarking Differential Privacy and Federated Learning for BERT Models. CoRR abs/2106.13973 (2021) - [i8]Aman Priyanshu, Rakshit Naidu, Fatemehsadat Mireshghallah, Mohammad Malekzadeh:
Efficient Hyperparameter Optimization for Differentially Private Deep Learning. CoRR abs/2108.03888 (2021) - [i7]Fatemehsadat Mireshghallah, Taylor Berg-Kirkpatrick:
Style Pooling: Automatic Text Style Obfuscation for Improved Classification Fairness. CoRR abs/2109.04624 (2021) - [i6]Fatemehsadat Mireshghallah, Vaishnavi Shrivastava, Milad Shokouhi, Taylor Berg-Kirkpatrick, Robert Sim, Dimitrios Dimitriadis:
UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis. CoRR abs/2110.00135 (2021) - 2020
- [j2]Ahmed T. Elthakeb, Prannoy Pilligundla, Fatemehsadat Mireshghallah, Amir Yazdanbakhsh, Hadi Esmaeilzadeh:
ReLeQ : A Reinforcement Learning Approach for Automatic Deep Quantization of Neural Networks. IEEE Micro 40(5): 37-45 (2020) - [c3]Fatemehsadat Mireshghallah, Mohammadkazem Taram, Prakash Ramrakhyani, Ali Jalali, Dean M. Tullsen, Hadi Esmaeilzadeh:
Shredder: Learning Noise Distributions to Protect Inference Privacy. ASPLOS 2020: 3-18 - [c2]Tom Farrand, Fatemehsadat Mireshghallah, Sahib Singh, Andrew Trask:
Neither Private Nor Fair: Impact of Data Imbalance on Utility and Fairness in Differential Privacy. PPMLP@CCS 2020: 15-19 - [c1]Ahmed Taha Elthakeb, Prannoy Pilligundla, Fatemeh Mireshghallah, Alexander Cloninger, Hadi Esmaeilzadeh:
Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks. ICML 2020: 2880-2891 - [i5]Ahmed T. Elthakeb, Prannoy Pilligundla, Fatemehsadat Mireshghallah, Tarek Elgindi, Charles-Alban Deledalle, Hadi Esmaeilzadeh:
Gradient-Based Deep Quantization of Neural Networks through Sinusoidal Adaptive Regularization. CoRR abs/2003.00146 (2020) - [i4]Fatemehsadat Mireshghallah, Mohammadkazem Taram, Ali Jalali, Ahmed Taha Elthakeb, Dean M. Tullsen, Hadi Esmaeilzadeh:
A Principled Approach to Learning Stochastic Representations for Privacy in Deep Neural Inference. CoRR abs/2003.12154 (2020) - [i3]Fatemehsadat Mireshghallah, Mohammadkazem Taram, Praneeth Vepakomma, Abhishek Singh, Ramesh Raskar, Hadi Esmaeilzadeh:
Privacy in Deep Learning: A Survey. CoRR abs/2004.12254 (2020) - [i2]Tom Farrand, Fatemehsadat Mireshghallah, Sahib Singh, Andrew Trask:
Neither Private Nor Fair: Impact of Data Imbalance on Utility and Fairness in Differential Privacy. CoRR abs/2009.06389 (2020)
2010 – 2019
- 2019
- [j1]Fatemehsadat Mireshghallah, Mohammad Bakhshalipour, Mohammad Sadrosadati, Hamid Sarbazi-Azad:
Energy-Efficient Permanent Fault Tolerance in Hard Real-Time Systems. IEEE Trans. Computers 68(10): 1539-1545 (2019) - [i1]Fatemehsadat Mireshghallah, Mohammadkazem Taram, Prakash Ramrakhyani, Dean M. Tullsen, Hadi Esmaeilzadeh:
Shredder: Learning Noise to Protect Privacy with Partial DNN Inference on the Edge. CoRR abs/1905.11814 (2019)
Coauthor Index
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last updated on 2023-09-08 12:24 CEST by the dblp team
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