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An unsolved challenge in distributed or federated learning is to effectively mitigate privacy risks without slowing down training or reducing accuracy.
RoBERTa: A robustly optimized BERT pretraining approach
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Advances and open problems in federated learning
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Health insurance portability and accountability act of 1996
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Which problems have strongly exponential complexity?
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. 1998 · 1998
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iDLG: Improved deep leakage from gradients
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen. 2020 · 2001
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Fine-tuning pretrained language models: Weight initializations, data orders, and early stopping
Jesse Dodge, Gabriel Ilharco, Roy Schwartz, Ali Farhadi, Hannaneh Hajishirzi, and Noah Smith. 2020 · 2002
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The random oracle methodology, revisited
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The PASCAL recognising textual entailment challenge
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Automatically constructing a corpus of sentential paraphrases
William B Dolan and Chris Brockett. 2005 · 2005
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The second PASCAL recognising textual entailment challenge
Roy Bar Haim, Ido Dagan, Bill Dolan, Lisa Ferro, Danilo Giampiccolo, Bernardo Magnini, and Idan Szpektor. 2006 · 2006
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Our data, ourselves: Privacy via distributed noise generation
Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor. 2006 · 2006
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The third PASCAL recognizing textual entailment challenge
Danilo Giampiccolo, Bernardo Magnini, Ido Dagan, and Bill Dolan. 2007 · 2007
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The differential privacy frontier
Cynthia Dwork. 2009 · 2009
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Fully homomorphic encryption using ideal lattices
Craig Gentry. 2009 · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky. 2009 · 2009
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Secret-sharing schemes: a survey
Amos Beimel. 2011 · 2011
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The complexity of linear dependence problems in vector spaces
Arnab Bhattacharyya, Piotr Indyk, David P Woodruff, and Ning Xie. 2011 · 2011
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The Winograd schema challenge
Hector J Levesque, Ernest Davis, and Leora Morgenstern. 2011 · 2011
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Ml confidential: Machine learning on encrypted data
Thore Graepel, Kristin Lauter, and Michael Naehrig. 2012 · 2012
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Electronic health records: privacy, confidentiality, and security
Laurinda B Harman, Cathy A Flite, and Kesa Bond. 2012 · 2012
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Exact weight subgraphs and the k k -sum conjecture
Amir Abboud and Kevin Lewi. 2013 · 2013
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Ng, and Christopher Potts. 2013 · 2013
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Losing weight by gaining edges
Amir Abboud, Kevin Lewi, and Ryan Williams. 2014 · 2014
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Federated learning for mobile keyboard prediction
Andrew Hard, Kanishka Rao, Rajiv Mathews, Swaroop Ramaswamy, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage. 2018 · 2018
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California consumer privacy act (ccpa)
California State Legislature. 2018 · 2018
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Towards robust and privacy-preserving text representations
Yitong Li, Timothy Baldwin, and Trevor Cohn. 2018 · 2018
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Learning differentially private recurrent language models
H Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang. 2018 · 2018
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel R. Bowman. 2018 · 2018
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The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth. 2014 · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2015 · 2015
Cited alongside, same era.
Privacy-preserving deep learning
Reza Shokri and Vitaly Shmatikov. 2015 · 2015
Cited alongside, same era.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016 · 2016
Cited alongside, same era.
The social impact of natural language processing
Dirk Hovy and Shannon L Spruit. 2016 · 2016
Cited alongside, same era.
SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
Cited alongside, same era.
Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review
Cao Xiao, Edward Choi, and Jimeng Sun. 2018 · 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 · 2019
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Privacy-preserving secret shared computations using mapreduce
Shlomi Dolev, Peeyush Gupta, Yin Li, Sharad Mehrotra, and Shantanu Sharma. 2019 · 2019
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Deep reinforcement learning-based text anonymization against private-attribute inference
Ahmadreza Mosallanezhad, Ghazaleh Beigi, and Huan Liu. 2019 · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019 · 2019
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Sentence-BERT: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
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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 · 2019
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Neural network acceptability judgments
Alex Warstadt, Amanpreet Singh, and Samuel R. Bowman. 2019 · 2019
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HuggingFace’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al. 2019 · 2019
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Deep leakage from gradients
Ligeng Zhu, Zhijian Liu, and Song Han. 2019 · 2019
Later among the works it cites.
Obfuscation for privacy-preserving syntactic parsing
Zhifeng Hu, Serhii Havrylov, Ivan Titov, and Shay B Cohen. 2020 · 2020
Closest in time.
Instahide: Instance-hiding schemes for private distributed learning
Yangsibo Huang, Zhao Song, Kai Li, and Sanjeev Arora. 2020 · 2020
Closest in time.
SpanBERT: Improving pre-training by representing and predicting spans
Mandar Joshi, Danqi Chen, Yinhan Liu, Daniel S Weld, Luke Zettlemoyer, and Omer Levy. 2020 · 2020
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Privacy risks of general-purpose language models
Xudong Pan, Mi Zhang, Shouling Ji, and Min Yang. 2020 · 2020
Closest in time.
Making the shoe fit: Architectures, initializations, and tuning for learning with privacy
Nicolas Papernot, Steve Chien, Shuang Song, Abhradeep Thakurta, and Ulfar Erlingsson. 2020 · 2020
Closest in time.
Information leakage in embedding models
Congzheng Song and Ananth Raghunathan. 2020 · 2020
Closest in time.