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Differentially private training algorithms like DP-SGD protect sensitive training data by ensuring that trained models do not reveal private information.
Towards automatic generation of shareable synthetic clinical notes using neural language models
Oren Melamud and Chaitanya Shivade · 1905
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu · 1910
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2005
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The trade-offs of private prediction
Laurens van der Maaten and Awni Y. Hannun · 2007
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A simple and practical algorithm for differentially private data release
Moritz Hardt, Katrina Ligett, and Frank McSherry · 2010
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A similarity measure for indefinite rankings
William Webber, Alistair Moffat, and Justin Zobel · 2010
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A learning theory approach to non-interactive database privacy
Avrim Blum, Katrina Ligett, and Aaron Roth · 2011
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Learning word vectors for sentiment analysis, 2011
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts · 2011
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Differentially private synthetic medical data generation using convolutional gans
Amirsina Torfi, Edward A. Fox, and Chandan K. Reddy · 2012
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Private empirical risk minimization: Efficient algorithms and tight error bounds
Raef Bassily, Adam Smith, and Abhradeep Thakurta · 2014
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Fingerprinting codes and the price of approximate differential privacy
Mark Bun, Jonathan Ullman, and Salil Vadhan · 2014
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Semi-supervised convolutional neural networks for text categorization via region embedding
Rie Johnson and Tong Zhang · 2015
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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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Practical locally private heavy hitters
Raef Bassily, Kobbi Nissim, Uri Stemmer, and Abhradeep Guha Thakurta · 2017
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Learning with privacy at scale
Apple Differential Privacy Team · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin · 2017
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Locally differentially private protocols for frequency estimation
T. Wang, J. Blocki, N. Li, and S. Jha · 2017
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Generation of synthetic electronic medical record text
Jiaqi Guan, Runzhe Li, Sheng Yu, and Xuegong Zhang · 2018
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Adafactor: Adaptive learning rates with sublinear memory cost
Noam Shazeer and Mitchell Stern · 2018
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Differentially private generative adversarial network
Liyang Xie, Kaixiang Lin, Shu Wang, Fei Wang, and Jiayu Zhou · 2018
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Towards private synthetic text generation
Rishi Bommasani, Steven Wu, and Xanda Schofield · 2019
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The secret sharer: Evaluating and testing unintended memorization in neural networks
Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, and Dawn Song · 2019
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Sentence-t5: Scalable sentence encoders from pre-trained text-to-text models
Jianmo Ni, Gustavo Hernández Ábrego, Noah Constant, Ji Ma, Keith B Hall, Daniel Cer, and Yinfei Yang · 2021
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Mauve: Measuring the gap between neural text and human text using divergence frontiers
Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, John Thickstun, Sean Welleck, Yejin Choi, and Zaid Harchaoui · 2021
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Benchmarking differentially private synthetic data generation algorithms
Yuchao Tao, Ryan McKenna, Michael Hay, Ashwin Machanavajjhala, and Gerome Miklau · 2021
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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, Sergey Yekhanin, and Huishuai Zhang · 2021
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Joshua Peterson, Stephan Meylan, and David Bourgin · 2019
Cited alongside, same era.
Gpt-2 model card, "language models are unsupervised multitask learners", 2019
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
Cited alongside, same era.
PATE-GAN: Generating synthetic data with differential privacy guarantees
Jinsung Yoon, James Jordon, and Mihaela van der Schaar · 2019
Cited alongside, same era.
The Pile: An 800gb dataset of diverse text for language modeling
Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, Shawn Presser, and Connor Leahy · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu · 2020
Cited alongside, same era.
AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts
Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, and Sameer Singh · 2020
Cited alongside, same era.
Extracting training data from large language models
Nicholas Carlini, Florian Tramèr, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Úlfar Erlingsson, Alina Oprea, and Colin Raffel · 2021
Cited alongside, same era.
Factual probing is [MASK]: Learning vs. learning to recall
Zexuan Zhong, Dan Friedman, and Danqi Chen · 2021
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GPT-NeoX-20B: An open-source autoregressive language model
Sid Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, Michael Pieler, USVSN Sai Prashanth, Shivanshu Purohit, Laria Reynolds, Jonathan Tow, Ben Wang, and Samuel Weinbach · 2022
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Measuring the measuring tools: An automatic evaluation of semantic metrics for text corpora
George Kour, Samuel Ackerman, Orna Raz, Eitan Farchi, Boaz Carmeli, and Ateret Anaby-Tavor · 2022
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Deduplicating training data makes language models better
Katherine Lee, Daphne Ippolito, Andrew Nystrom, Chiyuan Zhang, Douglas Eck, Chris Callison-Burch, and Nicholas Carlini · 2022
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Differentially private language models for secure data sharing
Justus Mattern, Zhijing Jin, Benjamin Weggenmann, Bernhard Schoelkopf, and Mrinmaya Sachan · 2022
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Large scale transfer learning for differentially private image classification, 2022
Harsh Mehta, Abhradeep Thakurta, Alexey Kurakin, and Ashok Cutkosky · 2022
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Training text-to-text transformers with privacy guarantees
Natalia Ponomareva, Jasmijn Bastings, and Sergei Vassilvitskii · 2022
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Scaling up models and data with t5x
Adam Roberts, Hyung Won Chung, Anselm Levskaya, Gaurav Mishra, James Bradbury, Daniel Andor, Sharan Narang, Brian Lester, Colin Gaffney, Afroz Mohiuddin, Curtis Hawthorne, Aitor Lewkowycz, Alex Salcianu, Marc van Zee, Jacob Austin, Sebastian Goodman, Livio Baldini Soares, Haitang Hu, Sasha Tsvyashchenko, Aakanksha Chowdhery, Jasmijn Bastings, Jannis Bulian, Xavier Garcia, Jianmo Ni, Andrew Chen, Kathleen Kenealy, Jonathan H. Clark, Stephan Lee, Dan Garrette, James Lee-Thorp, Colin Raffel, Noam Shazeer, Marvin Ritter, Maarten Bosma, Alexandre Passos, Jeremy Maitin-Shepard, Noah Fiedel, Mark Omernick, Brennan Saeta, Ryan Sepassi, Alexander Spiridonov, Joshua Newlan, and Andrea Gesmundo · 2022
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Lamda: Language models for dialog applications
Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, YaGuang Li, Hongrae Lee, Huaixiu Steven Zheng, Amin Ghafouri, Marcelo Menegali, Yanping Huang, Maxim Krikun, Dmitry Lepikhin, James Qin, Dehao Chen, Yuanzhong Xu, Zhifeng Chen, Adam Roberts, Maarten Bosma, Yanqi Zhou, Chung-Ching Chang, Igor Krivokon, Will Rusch, Marc Pickett, Kathleen S. Meier-Hellstern, Meredith Ringel Morris, Tulsee Doshi, Renelito Delos Santos, Toju Duke, Johnny Soraker, Ben Zevenbergen, Vinodkumar Prabhakaran, Mark Diaz, Ben Hutchinson, Kristen Olson, Alejandra Molina, Erin Hoffman-John, Josh Lee, Lora Aroyo, Ravi Rajakumar, Alena Butryna, Matthew Lamm, Viktoriya Kuzmina, Joe Fenton, Aaron Cohen, Rachel Bernstein, Ray Kurzweil, Blaise Aguera-Arcas, Claire Cui, Marian Croak, Ed H. Chi, and Quoc Le · 2022
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Synthetic text generation with differential privacy: A simple and practical recipe, 2022
Xiang Yue, Huseyin A. Inan, Xuechen Li, Girish Kumar, Julia McAnallen, Huan Sun, David Levitan, and Robert Sim · 2022
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How to dp-fy ml: A practical guide to machine learning with differential privacy, 2023
Natalia Ponomareva, Hussein Hazimeh, Alex Kurakin, Zheng Xu, Carson Denison, H. Brendan McMahan, Sergei Vassilvitskii, Steve Chien, and Abhradeep Thakurta · 2023
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Differentially private conditional text generation for synthetic data production, 2023
Pranav Putta, Ander Steele, and Joseph W Ferrara · 2023
Closest in time.