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We study the problem of private distribution learning with access to public data.
Densité et dimension
Patrick Assouad · 1983
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Rates of convergence of minimum distance estimators and Kolmogorov’s entropy
Yannis G. Yatracos · 1985
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Relating data compression and learnability, 1986
Nick Littlestone and Manfred Warmuth · 1986
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Combinatorial Methods in Density Estimation
Luc Devroye and Gábor Lugosi · 2001
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A spectral algorithm for learning mixtures of distributions
Santosh Vempala and Grant Wang · 2002
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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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Smooth sensitivity and sampling in private data analysis
Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2007
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A discriminative framework for clustering via similarity functions
Maria-Florina Balcan, Avrim Blum, and Santosh Vempala · 2008
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On the geometry of differential privacy
Moritz Hardt and Kunal Talwar · 2010
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Concise formulas for the area and volume of a hyperspherical cap
S. Li · 2011
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A learning theory approach to noninteractive database privacy
Avrim Blum, Katrina Ligett, and Aaron Roth · 2013
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Differential privacy based on importance weighting
Zhanglong Ji and Charles Elkan · 2013
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The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 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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Private learning and sanitization: Pure vs. approximate differential privacy
Amos Beimel, Kobbi Nissim, and Uri Stemmer · 2016
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Concentrated differential privacy: Simplifications, extensions, and lower bounds
Mark Bun and Thomas Steinke · 2016
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Sample compression schemes for VC classes
Shay Moran and Amir Yehudayoff · 2016
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BLENDER: Enabling local search with a hybrid differential privacy model
Brendan Avent, Aleksandra Korolova, David Zeber, Torgeir Hovden, and Benjamin Livshits · 2017
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Learning with privacy at scale, 2017
Differential Privacy Team, Apple · 2017
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Learning from untrusted data
Moses Charikar, Jacob Steinhardt, and Gregory Valiant · 2017
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Better agnostic clustering via relaxed tensor norms, 2017
Pravesh K. Kothari and Jacob Steinhardt · 2017
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Semi-supervised knowledge transfer for deep learning from private training data
Nicolas Papernot, Martín Abadi, Ulfar Erlingsson, Ian Goodfellow, and Kunal Talwar · 2017
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The U.S. Census Bureau adopts differential privacy
John M. Abowd · 2018
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Some techniques in density estimation
Hassan Ashtiani and Abbas Mehrabian · 2018
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Model-agnostic private learning
Raef Bassily, Om Thakkar, and Abhradeep Guha Thakurta · 2018
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List-decodable robust mean estimation and learning mixtures of spherical Gaussians
Ilias Diakonikolas, Daniel M. Kane, and Alistair Stewart · 2018
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The total variation distance between high-dimensional Gaussians
Luc Devroye, Abbas Mehrabian, and Tommy Reddad · 2018
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Finite sample differentially private confidence intervals
Vishesh Karwa and Salil P. Vadhan · 2018
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Scalable private learning with PATE
Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Úlfar Erlingsson · 2018
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Limits of private learning with access to public data
Noga Alon, Raef Bassily, and Shay Moran · 2019
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Private hypothesis selection
Mark Bun, Gautam Kamath, Thomas Steinke, and Zhiwei Steven Wu · 2019
Scalable differential privacy with sparse network finetuning
Zelun Luo, Daniel J Wu, Ehsan Adeli, and Li Fei-Fei · 2021
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Differentially private learning needs better features (or much more data)
Florian Tramèr and Dan Boneh · 2021
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Do not let privacy overbill utility: Gradient embedding perturbation for private learning
Da Yu, Huishuai Zhang, Wei Chen, and Tie-Yan Liu · 2021
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Bypassing the ambient dimension: Private SGD with gradient subspace identification
Yingxue Zhou, Zhiwei Steven Wu, and Arindam Banerjee · 2021
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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 · 2022
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Private and polynomial time algorithms for learning Gaussians and beyond
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Cited alongside, same era.
Diffprivlib: The IBM differential privacy library
Naoise Holohan, Stefano Braghin, Pól Mac Aonghusa, and Killian Levacher · 2019
Cited alongside, same era.
Privately learning high-dimensional distributions
Gautam Kamath, Jerry Li, Vikrant Singhal, and Jonathan R. Ullman · 2019
Cited alongside, same era.
Differentially private algorithms for learning mixtures of separated Gaussians
Gautam Kamath, Or Sheffet, Vikrant Singhal, and Jonathan Ullman · 2019
Cited alongside, same era.
Making the shoe fit: Architectures, initializations, and tuning for learning with privacy
Nicolas Papernot, Steve Chien, Shuang Song, Abhradeep Thakurta, and Ulfar Erlingsson · 2019
Cited alongside, same era.
On learnability wih computable learners
Sushant Agarwal, Nivasini Ananthakrishnan, Shai Ben-David, Tosca Lechner, and Ruth Urner · 2020
Cited alongside, same era.
Near-optimal sample complexity bounds for robust learning of gaussian mixtures via compression schemes
Hassan Ashtiani, Shai Ben-David, Nicholas J. A. Harvey, Christopher Liaw, Abbas Mehrabian, and Yaniv Plan · 2020
Cited alongside, same era.
Hassan Ashtiani and Christopher Liaw · 2022
Later among the works it cites.
Private estimation with public data
Alex Bie, Gautam Kamath, and Vikrant Singhal · 2022
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A private and computationally-efficient estimator for unbounded gaussians
Gautam Kamath, Argyris Mouzakis, Vikrant Singhal, Thomas Steinke, and Jonathan R. Ullman · 2022
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Private robust estimation by stabilizing convex relaxations
Pravesh K. Kothari, Pasin Manurangsi, and Ameya Velingker · 2022
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Transfer learning in differential privacy’s hybrid-model
Refael Kohen and Or Sheffet · 2022
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Differential privacy and robust statistics in high dimensions
Xiyang Liu, Weihao Kong, and Sewoong Oh · 2022
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Large language models can be strong differentially private learners
Xuechen Li, Florian Tramèr, Percy Liang, and Tatsunori Hashimoto · 2022
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FriendlyCore: Practical differentially private aggregation
Eliad Tsfadia, Edith Cohen, Haim Kaplan, Yishay Mansour, and Uri Stemmer · 2022
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Considerations for differentially private learning with large-scale public pretraining
Florian Tramèr, Gautam Kamath, and Nicholas Carlini · 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, Sergey Yekhanin, and Huishuai Zhang · 2022
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Polynomial time and private learning of unbounded gaussian mixture models, 2023
Jamil Arbas, Hassan Ashtiani, and Christopher Liaw · 2023
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Private estimation algorithms for stochastic block models and mixture models
Hongjie Chen, Vincent Cohen-Addad, Tommaso d’Orsi, Alessandro Epasto, Jacob Imola, David Steurer, and Stefan Tiegel · 2023
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Challenges towards the next frontier in privacy
Rachel Cummings, Damien Desfontaines, David Evans, Roxana Geambasu, Matthew Jagielski, Yangsibo Huang, Peter Kairouz, Gautam Kamath, Sewoong Oh, Olga Ohrimenko, Nicolas Papernot, Ryan Rogers, Milan Shen, Shuang Song, Weijie Su, Andreas Terzis, Abhradeep Thakurta, Sergei Vassilvitskii, Yu-Xiang Wang, Li Xiong, Sergey Yekhanin, Da Yu, Huanyu Zhang, and Wanrong Zhang · 2023
Closest in time.
Choosing public datasets for private machine learning via gradient subspace distance
Xin Gu, Gautam Kamath, and Zhiwei Steven Wu · 2023
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Google’s differential privacy libraries
Google · 2023
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TensorFlow privacy
Google · 2023
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Robustness implies privacy in statistical estimation
Samuel B. Hopkins, Gautam Kamath, Mahbod Majid, and Shyam Narayanan · 2023
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Optimal differentially private learning with public data
Andrew Lowy, Zeman Li, Tianjian Huang, and Meisam Razaviyayn · 2023
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Federated learning of Gboard language models with differential privacy
Zheng Xu, Yanxiang Zhang, Galen Andrew, Christopher A. Choquette-Choo, Peter Kairouz, H. Brendan McMahan, Jesse Rosenstock, and Yuanbo Zhang · 2023
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