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As the use of large embedding models in recommendation systems and language applications increases, concerns over user data privacy have also risen.
Our data, ourselves: Privacy via distributed noise generation
Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor · 2006
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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Releasing search queries and clicks privately
Aleksandra Korolova, Krishnaram Kenthapadi, Nina Mishra, and Alexandros Ntoulas · 2009
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Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean · 2013
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Y Wu, Jason Chuang, Christopher D Manning, Andrew Ng, and Christopher Potts · 2013
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning · 2014
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Wide & deep learning for recommender systems
Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, et al · 2016
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Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 2016
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DeepFM: a factorization-machine based neural network for CTR prediction
Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He · 2017
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First Quora Dataset Release: Question Pairs
Srinivasan Iyer, Mark Yatskar, John Wieting, Sheng-Fu Chang, and Nazneen Guo · 2017
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Deep & cross network for ad click predictions
Ruoxi Wang, Bin Fu, Gang Fu, and Mingliang Wang · 2017
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XNLI: evaluating cross-lingual sentence representations
Alexis Conneau, Ruty Rinott, Guillaume Lample, Adina Williams, Samuel R. Bowman, Holger Schwenk, and Veselin Stoyanov · 2018
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Deep interest network for click-through rate prediction
Guorui Zhou, Xiaoqiang Zhu, Chenru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai · 2018
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BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Jinshuo Dong, Aaron Roth, and Weijie J. Su · 2019
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Accelerating wide & deep recommender inference on GPUs, 2019
Alec Gunny, Chirayu Garg, Levs Dolgovs, and Akshay Subramaniam · 2019
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Parameter-efficient transfer learning for NLP
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly · 2019
Earlier work this paper cites.
RoBERTa: a robustly optimized BERT pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
Earlier work this paper cites.
Deep learning recommendation model for personalization and recommendation systems
Maxim Naumov, Dheevatsa Mudigere, Hao-Jun Michael Shi, Jianyu Huang, Narayanan Sundaraman, Jongsoo Park, Xiaodong Wang, Udit Gupta, Carole-Jean Wu, Alisson G Azzolini, et al · 2019
Cited alongside, same era.
GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman · 2019
Cited alongside, same era.
Unsupervised cross-lingual representation learning at scale
Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov · 2020
Cited alongside, same era.
Differentially private set union
Sivakanth Gopi, Pankaj Gulhane, Janardhan Kulkarni, Judy Hanwen Shen, Milad Shokouhi, and Sergey Yekhanin · 2020
Cited alongside, same era.
DP Accounting Library
Google’s DP Library · 2020
Cited alongside, same era.
Connect the dots: Tighter discrete approximations of privacy loss distributions
Vadym Doroshenko, Badih Ghazi, Pritish Kamath, Ravi Kumar, and Pasin Manurangsi · 2022
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Differentially private partition selection
Damien Desfontaines, James Voss, Bryant Gipson, and Chinmoy Mandayam · 2022
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Recovering private text in federated learning of language models
Samyak Gupta, Yangsibo Huang, Zexuan Zhong, Tianyu Gao, Kai Li, and Danqi Chen · 2022
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Faster privacy accounting via evolving discretization
Badih Ghazi, Pritish Kamath, Ravi Kumar, and Pasin Manurangsi · 2022
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Introduction to Online Convex Optimization
Elad Hazan · 2022
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Are large pre-trained language models leaking your personal information?
Jie Huang, Hanyin Shao, and Kevin Chen-Chuan Chang · 2022
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Computing tight differential privacy guarantees using FFT
Antti Koskela, Joonas Jälkö, and Antti Honkela · 2020
Cited alongside, same era.
Distributed hierarchical GPU parameter server for massive scale deep learning ads systems
Weijie Zhao, Deping Xie, Ronglai Jia, Yulei Qian, Ruiquan Ding, Mingming Sun, and Ping Li · 2020
Cited alongside, same era.
Differentially private weighted sampling
Edith Cohen, Ofir Geri, Tamas Sarlos, and Uri Stemmer · 2021
Cited alongside, same era.
Extracting training data from large language models
Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom B Brown, Dawn Song, Ulfar Erlingsson, et al · 2021
Cited alongside, same era.
Not all noise is accounted equally: How differentially private learning benefits from large sampling rates
Friedrich Dörmann, Osvald Frisk, Lars Nørvang Andersen, and Christian Fischer Pedersen · 2021
Cited alongside, same era.
One-shot DP top- k k mechanisms
David Durfee and Ryan Rogers · 2021
Cited alongside, same era.
Numerical composition of differential privacy
Sivakanth Gopi, Yin Tat Lee, and Lukas Wutschitz · 2021
Cited alongside, same era.
Later among the works it cites.
LoRA: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2022
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Toward training at ImageNet scale with differential privacy
Alexey Kurakin, Steve Chien, Shuang Song, Roxana Geambasu, Andreas Terzis, and Abhradeep Thakurta · 2022
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Software-hardware co-design for fast and scalable training of deep learning recommendation models
Dheevatsa Mudigere, Yuchen Hao, Jianyu Huang, Zhihao Jia, Andrew Tulloch, Srinivas Sridharan, Xing Liu, Mustafa Ozdal, Jade Nie, Jongsoo Park, et al · 2022
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The graph-based behavior-aware recommendation for interactive news
Mingyuan Ma, Sen Na, Hongyu Wang, Congzhou Chen, and Jin Xu · 2022
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Eana: Reducing privacy risk on large-scale recommendation models
Lin Ning, Steve Chien, Shuang Song, Mei Chen, Yunqi Xue, and Devora Berlowitz · 2022
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Training large-scale recommendation models with TPUs, 2022
Snap Inc · 2022
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Merlin hugeCTR: GPU-accelerated recommender system training and inference
Zehuan Wang, Yingcan Wei, Minseok Lee, Matthias Langer, Fan Yu, Jie Liu, Shijie Liu, Daniel G Abel, Xu Guo, Jianbing Dong, et al · 2022
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Differentially private fine-tuning of language models
Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin A Inan, Gautam Kamath, Janardhan Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, et al · 2022
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Optimal accounting of differential privacy via characteristic function
Yuqing Zhu, Jinshuo Dong, and Yu-Xiang Wang · 2022
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Private ad modeling with DP-SGD
Carson Denison, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Krishna Giri Narra, Amer Sinha, Avinash Varadarajan, and Chiyuan Zhang · 2023
Closest in time.
TPU v4: an optically reconfigurable supercomputer for machine learning with hardware support for embeddings
Norm Jouppi, George Kurian, Sheng Li, Peter Ma, Rahul Nagarajan, Lifeng Nai, Nishant Patil, Suvinay Subramanian, Andy Swing, Brian Towles, et al · 2023
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How to DP-fy ML: A practical guide to machine learning with differential privacy
Natalia Ponomareva, Hussein Hazimeh, Alex Kurakin, Zheng Xu, Carson Denison, H. Brendan McMahan, Sergei Vassilvitskii, Steve Chien, and Abhradeep Guha Thakurta · 2023
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DP-SIPS: a simpler, more scalable mechanism for differentially private partition selection
Marika Swanberg, Damien Desfontaines, and Samuel Haney · 2023
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