Fetching the paper…
Reading the bibliography…
Differentially private mean estimation is an important building block in privacy-preserving algorithms for data analysis and machine learning.
Privately Learning High-Dimensional Distributions. In Proceedings of the Thirty-Second Conference on Learning Theory . PMLR, 1853–1902
Gautam Kamath, Jerry Li, Vikrant Singhal, and Jonathan Ullman. 2019 · 1902
Earlier work this paper cites.
AdaCliP: Adaptive Clipping for Private SGD
Venkatadheeraj Pichapati, Ananda Theertha Suresh, Felix X. Yu, Sashank J. Reddi, and Sanjiv Kumar. 2019 · 1908
Earlier work this paper cites.
Chi-square distributions including Chi and Rayleigh
NL Johnson, S Kotz, and N Balakrishnan. 1994 · 1994
Earlier work this paper cites.
Differentially Private Confidence Intervals
Wenxin Du, Canyon Foot, Monica Moniot, Andrew Bray, and Adam Groce. 2020 · 2001
Earlier work this paper cites.
Probability and Computing: Randomized Algorithms and Probabilistic Analysis
Michael Mitzenmacher and Eli Upfal. 2005 · 2005
Earlier work this paper cites.
Calibrating Noise to Sensitivity in Private Data Analysis. In Theory of Cryptography (Lecture Notes in Computer Science) , Shai Halevi and Tal Rabin (Eds.). Springer Berlin Heidelberg, 265–284
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith. 2006 · 2006
Earlier work this paper cites.
Concentration of Measure for the Analysis of Randomized Algorithms
Devdatt P. Dubhashi and Alessandro Panconesi. 2009 · 2009
Earlier work this paper cites.
Privacy-Preserving Statistical Estimation with Optimal Convergence Rates. In Proceedings of the Forty-Third Annual ACM Symposium on Theory of Computing (STOC ’11) . Association for Computing Machinery, New York, NY, USA, 813–822
Adam Smith. 2011 · 2011
Earlier work this paper cites.
Moments and absolute moments of the normal distribution
Andreas Winkelbauer. 2012 · 2012
Earlier work this paper cites.
Analyze Gauss: Optimal Bounds for Privacy-Preserving Principal Component Analysis. In Proceedings of the Forty-Sixth Annual ACM Symposium on Theory of Computing (STOC ’14) . Association for Computing Machinery, New York, NY, USA, 11–20
Cynthia Dwork, Kunal Talwar, Abhradeep Thakurta, and Li Zhang. 2014 · 2014
Earlier work this paper cites.
The Noisy Power Method: A Meta Algorithm with Applications. In Advances in Neural Information Processing Systems , Vol. 27. Curran Associates, Inc
Moritz Hardt and Eric Price. 2014 · 2014
Earlier work this paper cites.
Differentially Private Release and Learning of Threshold Functions. In 2015 IEEE 56th Annual Symposium on Foundations of Computer Science . 634–649
Mark Bun, Kobbi Nissim, Uri Stemmer, and Salil Vadhan. 2015 · 2015
Earlier work this paper cites.
Deep Learning with Differential Privacy. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (Vienna, Austria) (CCS ’16) . Association for Computing Machinery, New York, NY, USA, 308–318
Martin Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016 · 2016
Earlier work this paper cites.
Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds. In Theory of Cryptography (Lecture Notes in Computer Science) , Martin Hirt and Adam Smith (Eds.). Springer, Berlin, Heidelberg, 635–658
Mark Bun and Thomas Steinke. 2016 · 2016
Cited alongside, same era.
A Discrete Choice Model for Subset Selection. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining . ACM, Marina Del Rey CA USA, 37–45
Austin R. Benson, Ravi Kumar, and Andrew Tomkins. 2018 · 2018
Cited alongside, same era.
Finite Sample Differentially Private Confidence Intervals. In 9th Innovations in Theoretical Computer Science Conference (ITCS 2018) . Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik GmbH, Wadern/Saarbruecken, Germany, 9 pages
Vishesh Karwa and Salil Vadhan. 2018 · 2018
Cited alongside, same era.
Learning Differentially Private Recurrent Language Models
H. Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang. 2018 · 2018
Cited alongside, same era.
Privately Estimating a Gaussian: Efficient, Robust and Optimal
Daniel Alabi, Pravesh K. Kothari, Pranay Tankala, Prayaag Venkat, and Fred Zhang. 2022 · 2022
Later among the works it cites.
Private and Polynomial Time Algorithms for Learning Gaussians and Beyond. In Proceedings of Thirty Fifth Conference on Learning Theory . PMLR, 1075–1076
Hassan Ashtiani and Christopher Liaw. 2022 · 2022
Later among the works it cites.
Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger
Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, and George Karypis. 2022 · 2022
Later among the works it cites.
Mean Estimation with User-level Privacy under Data Heterogeneity
Rachel Cummings, Vitaly Feldman, Audra McMillan, and Kunal Talwar. 2022 · 2022
Later among the works it cites.
Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Differentially Private Covariance Estimation. In Advances in Neural Information Processing Systems , Vol. 32. Curran Associates, Inc
Kareem Amin, Travis Dick, Alex Kulesza, Andres Munoz, and Sergei Vassilvitskii. 2019 · 2019
Cited alongside, same era.
Instance-Optimality in Differential Privacy via Approximate Inverse Sensitivity Mechanisms. In Advances in Neural Information Processing Systems , Vol. 33. Curran Associates, Inc., 14106–14117
Hilal Asi and John C Duchi. 2020 · 2020
Cited alongside, same era.
CoinPress: Practical Private Mean and Covariance Estimation
Sourav Biswas, Yihe Dong, Gautam Kamath, and Jonathan Ullman. 2020 · 2020
Cited alongside, same era.
Private Mean Estimation of Heavy-Tailed Distributions. In Proceedings of Thirty Third Conference on Learning Theory . PMLR, 2204–2235
Gautam Kamath, Vikrant Singhal, and Jonathan Ullman. 2020 · 2020
Cited alongside, same era.
Differentially Private Learning with Adaptive Clipping. In Advances in Neural Information Processing Systems , Vol. 34. Curran Associates, Inc., 17455–17466
Galen Andrew, Om Thakkar, Brendan McMahan, and Swaroop Ramaswamy. 2021 · 2021
Cited alongside, same era.
Covariance-Aware Private Mean Estimation Without Private Covariance Estimation. In Advances in Neural Information Processing Systems , Vol. 34. Curran Associates, Inc., 7950–7964
Gavin Brown, Marco Gaboardi, Adam Smith, Jonathan Ullman, and Lydia Zakynthinou. 2021 · 2021
Cited alongside, same era.
The Cost of Privacy: Optimal Rates of Convergence for Parameter Estimation with Differential Privacy
T. Tony Cai, Yichen Wang, and Linjun Zhang. 2021 · 2021
Cited alongside, same era.
Instance-optimal Mean Estimation Under Differential Privacy
Ziyue Huang, Yuting Liang, and Ke Yi. 2021 · 2021
Cited alongside, same era.
Sergey Denisov, H Brendan McMahan, Keith Rush, Adam Smith, and Abhradeep Thakurta. 2022 · 2022
Later among the works it cites.
Efficient Mean Estimation with Pure Differential Privacy via a Sum-of-Squares Exponential Mechanism. In Proceedings of the 54th Annual ACM SIGACT Symposium on Theory of Computing (STOC 2022) . Association for Computing Machinery, New York, NY, USA, 1406–1417
Samuel B. Hopkins, Gautam Kamath, and Mahbod Majid. 2022 · 2022
Later among the works it cites.
A Private and Computationally-Efficient Estimator for Unbounded Gaussians. In Proceedings of Thirty Fifth Conference on Learning Theory . PMLR, 544–572
Gautam Kamath, Argyris Mouzakis, Vikrant Singhal, Thomas Steinke, and Jonathan Ullman. 2022 · 2022
Later among the works it cites.
Differentially Private Approximate Quantiles. In Proceedings of the 39th International Conference on Machine Learning . PMLR, 10751–10761
Haim Kaplan, Shachar Schnapp, and Uri Stemmer. 2022 · 2022
Later among the works it cites.
Private Robust Estimation by Stabilizing Convex Relaxations. In Proceedings of Thirty Fifth Conference on Learning Theory . PMLR, 723–777
Pravesh Kothari, Pasin Manurangsi, and Ameya Velingker. 2022 · 2022
Later among the works it cites.
Gavin Brown, Samuel B. Hopkins, and Adam Smith. 2023 · 2023
Closest in time.
A Fast Algorithm for Adaptive Private Mean Estimation
John Duchi, Saminul Haque, and Rohith Kuditipudi. 2023 · 2023
Closest in time.
A Bias-Variance-Privacy Trilemma for Statistical Estimation
Gautam Kamath, Argyris Mouzakis, Matthew Regehr, Vikrant Singhal, Thomas Steinke, and Jonathan Ullman. 2023 · 2023
Closest in time.
Multi-Task Differential Privacy Under Distribution Skew
Walid Krichene, Prateek Jain, Shuang Song, Mukund Sundararajan, Abhradeep Thakurta, and Li Zhang. 2023 · 2023
Closest in time.