Fetching the paper…
Reading the bibliography…
Large pretrained models can be privately fine-tuned to achieve performance approaching that of non-private models.
Applied numerical linear algebra
James W Demmel · 1997
Earlier work this paper cites.
Stability and generalization
Olivier Bousquet and André Elisseeff · 2002
Earlier work this paper cites.
Information-theoretic lower bounds on the oracle complexity of convex optimization
Alekh Agarwal, Martin J Wainwright, Peter Bartlett, and Pradeep Ravikumar · 2009
Earlier work this paper cites.
Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D Sarwate · 2011
Earlier work this paper cites.
Private convex empirical risk minimization and high-dimensional regression
Daniel Kifer, Adam Smith, and Abhradeep Thakurta · 2012
Earlier work this paper cites.
Stochastic gradient descent with differentially private updates
Shuang Song, Kamalika Chaudhuri, and Anand D Sarwate · 2013
Earlier work this paper cites.
Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Y Ng, and Christopher Potts · 2013
Earlier work this paper cites.
Private empirical risk minimization: Efficient algorithms and tight error bounds
Raef Bassily, Adam Smith, and Abhradeep Thakurta · 2014
Earlier work this paper cites.
The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
Earlier work this paper cites.
(near) dimension independent risk bounds for differentially private learning
Prateek Jain and Abhradeep Guha Thakurta · 2014
Earlier work this paper cites.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Differentially private empirical risk minimization with input perturbation
Kazuto Fukuchi, Quang Khai Tran, and Jun Sakuma · 2017
Earlier work this paper cites.
Learning differentially private recurrent language models
H Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 2017
Earlier work this paper cites.
The e2e dataset: New challenges for end-to-end generation
Jekaterina Novikova, Ondřej Dušek, and Verena Rieser · 2017
Earlier work this paper cites.
Bolt-on differential privacy for scalable stochastic gradient descent-based analytics
Xi Wu, Fengan Li, Arun Kumar, Kamalika Chaudhuri, Somesh Jha, and Jeffrey F. Naughton · 2017
Earlier work this paper cites.
Differentially private empirical risk minimization revisited: Faster and more general
Di Wang, Minwei Ye, and Jinhui Xu · 2017
Earlier work this paper cites.
Efficient private erm for smooth objectives
Jiaqi Zhang, Kai Zheng, Wenlong Mou, and Liwei Wang · 2017
Earlier work this paper cites.
Privacy amplification by subsampling: Tight analyses via couplings and divergences
Borja Balle, Gilles Barthe, and Marco Gaboardi · 2018
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Cited alongside, same era.
Gradient descent happens in a tiny subspace
Guy Gur-Ari, Daniel A Roberts, and Ethan Dyer · 2018
Cited alongside, same era.
Measuring the intrinsic dimension of objective landscapes
Chunyuan Li, Heerad Farkhoor, Rosanne Liu, and Jason Yosinski · 2018
Cited alongside, same era.
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever · 2018
Cited alongside, same era.
Private stochastic convex optimization with optimal rates
Raef Bassily, Vitaly Feldman, Kunal Talwar, and Abhradeep Thakurta · 2019
Cited alongside, same era.
Private stochastic convex optimization: Optimal rates in ℓ 1 \ell_{1} geometry
Hilal Asi, Vitaly Feldman, Tomer Koren, and Kunal Talwar · 2021
Later among the works it cites.
Public data-assisted mirror descent for private model training
Ehsan Amid, Arun Ganesh, Rajiv Mathews, Swaroop Ramaswamy, Shuang Song, Thomas Steinke, Vinith M. Suriyakumar, Om Thakkar, and Abhradeep Thakurta · 2021
Later among the works it cites.
Non-euclidean differentially private stochastic convex optimization
Raef Bassily, Cristóbal Guzmán, and Anupama Nandi · 2021
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 · 2021
Later among the works it cites.
(nearly) dimension independent private erm with adagrad rates via publicly estimated subspaces
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
An investigation into neural net optimization via hessian eigenvalue density
Behrooz Ghorbani, Shankar Krishnan, and Ying Xiao · 2019
Cited alongside, same era.
Diego Granziol, Xingchen Wan, and Timur Garipov · 2019
Cited alongside, same era.
Towards practical differentially private convex optimization
Roger Iyengar, Joseph P Near, Dawn Song, Om Thakkar, Abhradeep Thakurta, and Lun Wang · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
Cited alongside, same era.
Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf · 2019
Cited alongside, same era.
Intrinsic dimensionality explains the effectiveness of language model fine-tuning
Armen Aghajanyan, Luke Zettlemoyer, and Sonal Gupta · 2020
Cited alongside, same era.
Peter Kairouz, Monica Ribero Diaz, Keith Rush, and Abhradeep Thakurta · 2021
Later among the works it cites.
Janardhan Kulkarni, Yin Tat Lee, and Daogao Liu · 2021
Later among the works it cites.
Curse of dimensionality in unconstrained private convex erm
Daogao Liu and Zhou Lu · 2021
Later among the works it cites.
Large language models can be strong differentially private learners
Xuechen Li, Florian Tramer, Percy Liang, and Tatsunori Hashimoto · 2021
Later among the works it cites.
Leveraging public data for practical private query release
Terrance Liu, Giuseppe Vietri, Thomas Steinke, Jonathan Ullman, and Zhiwei Steven Wu · 2021
Later among the works it cites.
Scalable differential privacy with sparse network finetuning
Zelun Luo, Daniel J Wu, Ehsan Adeli, and Li Fei-Fei · 2021
Later among the works it cites.
Evading the curse of dimensionality in unconstrained private glms
Shuang Song, Thomas Steinke, Om Thakkar, and Abhradeep Thakurta · 2021
Later among the works it cites.
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 · 2021
Later among the works it cites.
Do not let privacy overbill utility: Gradient embedding perturbation for private learning
Da Yu, Huishuai Zhang, Wei Chen, and Tie-Yan Liu · 2021
Later among the works it cites.
Do not let privacy overbill utility: Gradient embedding perturbation for private learning
Da Yu, Huishuai Zhang, Wei Chen, and Tie-Yan Liu · 2021
Later among the works it cites.
Unlocking high-accuracy differentially private image classification through scale
Soham De, Leonard Berrada, Jamie Hayes, Samuel L Smith, and Borja Balle · 2022
Closest in time.
Private convex optimization via exponential mechanism
Sivakanth Gopi, Yin Tat Lee, and Daogao Liu · 2022
Closest in time.
Langevin diffusion: An almost universal algorithm for private euclidean (convex) optimization
Arun Ganesh, Abhradeep Thakurta, and Jalaj Upadhyay · 2022
Closest in time.
Dimension independent generalization of dp-sgd for overparameterized smooth convex optimization
Yi-An Ma, Teodor Vanislavov Marinov, and Tong Zhang · 2022
Closest in time.
Large scale transfer learning for differentially private image classification
Harsh Mehta, Abhradeep Thakurta, Alexey Kurakin, and Ashok Cutkosky · 2022
Closest in time.