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Differentially private empirical risk minimization (DP-ERM) is a fundamental problem in private optimization.
Collusion-secure fingerprinting for digital data
Dan Boneh and James Shaw · 1905
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Collusion-secure fingerprinting for digital data
Dan Boneh and James Shaw · 1905
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Norm-preserving extension of convex lipschitz functions
S Cobzas and C Mustata · 1978
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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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Concentration of measure and logarithmic sobolev inequalities
Michel Ledoux · 2006
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Optimal probabilistic fingerprint codes
Gábor Tardos · 2008
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What can we learn privately?
Shiva Prasad Kasiviswanathan, Homin K Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2011
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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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The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
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(near) dimension independent risk bounds for differentially private learning
Prateek Jain and Abhradeep Guha Thakurta · 2014
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Interactive fingerprinting codes and the hardness of preventing false discovery
Thomas Steinke and Jonathan Ullman · 2015
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Nearly-optimal private lasso
Kunal Talwar, Abhradeep Thakurta, and Li Zhang · 2015
Cited alongside, same era.
Sampling from strongly log-concave distributions with the unadjusted langevin algorithm
Alain Durmus and Eric Moulines · 2016
Cited alongside, same era.
Between pure and approximate differential privacy
Thomas Steinke and Jonathan Ullman · 2016
Cited alongside, same era.
Differentially private empirical risk minimization revisited: Faster and more general
Di Wang, Minwei Ye, and Jinhui Xu · 2017
Cited alongside, same era.
Fingerprinting codes and the price of approximate differential privacy
Mark Bun, Jonathan Ullman, and Salil Vadhan · 2018
Cited alongside, same era.
Private stochastic convex optimization with optimal rates
Raef Bassily, Vitaly Feldman, Kunal Talwar, and Abhradeep Guha Thakurta · 2019
Private stochastic convex optimization: Optimal rates in ℓ 1 \ell_{1} geometry
Hilal Asi, Vitaly Feldman, Tomer Koren, and Kunal Talwar · 2021
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The entropic barrier is n n -self-concordant
Sinho Chewi · 2021
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Janardhan Kulkarni, Yin Tat Lee, and Daogao Liu · 2021
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Evading the curse of dimensionality in unconstrained private glms
Shuang Song, Thomas Steinke, Om Thakkar, and Abhradeep Thakurta · 2021
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Private convex optimization via exponential mechanism
Sivakanth Gopi, Yin Tat Lee, and Daogao Liu · 2022
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Cited alongside, same era.
Introduction to online convex optimization
Elad Hazan · 2019
Cited alongside, same era.
Stability of stochastic gradient descent on nonsmooth convex losses
Raef Bassily, Vitaly Feldman, Cristóbal Guzmán, and Kunal Talwar · 2020
Cited alongside, same era.
Private stochastic convex optimization: optimal rates in linear time
Vitaly Feldman, Tomer Koren, and Kunal Talwar · 2020
Cited alongside, same era.
Dimension independence in unconstrained private erm via adaptive preconditioning
Peter Kairouz, Mónica Ribero, Keith Rush, and Abhradeep Thakurta · 2020
Cited alongside, same era.
Bypassing the ambient dimension: Private sgd with gradient subspace identification
Yingxue Zhou, Zhiwei Steven Wu, and Arindam Banerjee · 2020
Cited alongside, same era.
Differentially private stochastic optimization: New results in convex and non-convex settings
Raef Bassily, Cristóbal Guzmán, and Michael Menart
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Private streaming sco in ℓ _ p \ell\_p geometry with applications in high dimensional online decision making
Yuxuan Han, Zhicong Liang, Zhipeng Liang, Yang Wang, Yuan Yao, and Jiheng Zhang · 2022
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When does differentially private learning not suffer in high dimensions?
Xuechen Li, Daogao Liu, Tatsunori Hashimoto, Huseyin A Inan, Janardhan Kulkarni, Yin Tat Lee, and Abhradeep Guha Thakurta · 2022
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Private convex optimization in general norms
Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, and Kevin Tian · 2023
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Dpzero: Dimension-independent and differentially private zeroth-order optimization
Liang Zhang, Kiran Koshy Thekumparampil, Sewoong Oh, and Niao He · 2023
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Private fine-tuning of large language models with zeroth-order optimization
Xinyu Tang, Ashwinee Panda, Milad Nasr, Saeed Mahloujifar, and Prateek Mittal · 2024
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