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2020 – today
- 2024
[c11]Kunal Talwar
, Shan Wang, Audra McMillan, Vitaly Feldman, Pansy Bansal, Bailey Basile, Áine Cahill, Yi Sheng Chan, Mike Chatzidakis, Junye Chen, Oliver R. A. Chick, Mona Chitnis, Suman Ganta, Yusuf Goren, Filip Granqvist, Kristine Guo, Frederic Jacobs, Omid Javidbakht, Albert Liu, Richard Low, Dan Mascenik, Steve Myers, David Park, Wonhee Park, Gianni Parsa, Tommy Pauly, Christian Priebe, Rehan Rishi, Guy N. Rothblum, Congzheng Song, Linmao Song, Karl Tarbe, Sebastian Vogt, Shundong Zhou, Vojta Jina, Michael Scaria, Luke Winstrom:
Samplable Anonymous Aggregation for Private Federated Data Analysis. CCS 2024: 2859-2873- [c10]Karan N. Chadha, Junye Chen, John C. Duchi, Vitaly Feldman, Hanieh Hashemi, Omid Javidbakht, Audra McMillan, Kunal Talwar:
Differentially Private Heavy Hitter Detection using Federated Analytics. SaTML 2024: 512-533 - [i17]Vitaly Feldman, Audra McMillan, Satchit Sivakumar, Kunal Talwar:
Instance-Optimal Private Density Estimation in the Wasserstein Distance. CoRR abs/2406.19566 (2024) - 2023
- [c9]Vitaly Feldman, Audra McMillan, Kunal Talwar:
Stronger Privacy Amplification by Shuffling for Renyi and Approximate Differential Privacy. SODA 2023: 4966-4981 - [i16]Karan N. Chadha, Junye Chen, John C. Duchi, Vitaly Feldman, Hanieh Hashemi, Omid Javidbakht, Audra McMillan, Kunal Talwar:
Differentially Private Heavy Hitter Detection using Federated Analytics. CoRR abs/2307.11749 (2023) - [i15]Kunal Talwar, Shan Wang, Audra McMillan, Vojta Jina, Vitaly Feldman, Bailey Basile, Áine Cahill, Yi Sheng Chan, Mike Chatzidakis, Junye Chen, Oliver R. A. Chick, Mona Chitnis, Suman Ganta, Yusuf Goren, Filip Granqvist, Kristine Guo, Frederic Jacobs, Omid Javidbakht, Albert Liu, Richard Low, Dan Mascenik, Steve Myers, David Park, Wonhee Park, Gianni Parsa, Tommy Pauly, Christian Priebe, Rehan Rishi, Guy N. Rothblum, Michael Scaria, Linmao Song, Congzheng Song, Karl Tarbe, Sebastian Vogt, Luke Winstrom, Shundong Zhou:
Samplable Anonymous Aggregation for Private Federated Data Analysis. CoRR abs/2307.15017 (2023) - [i14]Rachel Cummings, Vitaly Feldman, Audra McMillan, Kunal Talwar:
Mean Estimation with User-level Privacy under Data Heterogeneity. CoRR abs/2307.15835 (2023) - 2022
- [j1]Daniel Alabi, Audra McMillan, Jayshree Sarathy, Adam D. Smith, Salil P. Vadhan:
Differentially Private Simple Linear Regression. Proc. Priv. Enhancing Technol. 2022(2): 184-204 (2022) - [c8]Mark Bun, Jörg Drechsler, Marco Gaboardi, Audra McMillan, Jayshree Sarathy:
Controlling Privacy Loss in Sampling Schemes: An Analysis of Stratified and Cluster Sampling. FORC 2022: 1:1-1:24 - [c7]Rachel Cummings, Vitaly Feldman, Audra McMillan, Kunal Talwar:
Mean Estimation with User-level Privacy under Data Heterogeneity. NeurIPS 2022 - [i13]Vitaly Feldman, Audra McMillan, Kunal Talwar:
Stronger Privacy Amplification by Shuffling for Rényi and Approximate Differential Privacy. CoRR abs/2208.04591 (2022) - [i12]Audra McMillan, Adam D. Smith, Jonathan R. Ullman:
Instance-Optimal Differentially Private Estimation. CoRR abs/2210.15819 (2022) - [i11]Audra McMillan, Omid Javidbakht, Kunal Talwar, Elliot Briggs, Mike Chatzidakis, Junye Chen, John C. Duchi, Vitaly Feldman, Yusuf Goren, Michael Hesse, Vojta Jina, Anil Katti, Albert Liu, Cheney Lyford, Joey Meyer, Alex Palmer, David Park, Wonhee Park, Gianni Parsa, Paul Pelzl, Rehan Rishi, Congzheng Song, Shan Wang, Shundong Zhou:
Private Federated Statistics in an Interactive Setting. CoRR abs/2211.10082 (2022) - 2021
- [c6]Vitaly Feldman, Audra McMillan, Kunal Talwar:
Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling. FOCS 2021: 954-964 - [i10]Jörg Drechsler, Ira Globus-Harris, Audra McMillan, Jayshree Sarathy, Adam D. Smith:
Non-parametric Differentially Private Confidence Intervals for the Median. CoRR abs/2106.10333 (2021) - 2020
- [c5]Clément L. Canonne, Gautam Kamath, Audra McMillan, Jonathan R. Ullman, Lydia Zakynthinou:
Private Identity Testing for High-Dimensional Distributions. NeurIPS 2020 - [i9]Daniel Alabi, Audra McMillan, Jayshree Sarathy, Adam D. Smith, Salil P. Vadhan:
Differentially Private Simple Linear Regression. CoRR abs/2007.05157 (2020) - [i8]Mark Bun, Jörg Drechsler, Marco Gaboardi, Audra McMillan:
Controlling Privacy Loss in Survey Sampling (Working Paper). CoRR abs/2007.12674 (2020) - [i7]Vitaly Feldman, Audra McMillan, Kunal Talwar:
Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling. CoRR abs/2012.12803 (2020)
2010 – 2019
- 2019
- [c4]Jacob D. Abernethy, Young Hun Jung, Chansoo Lee, Audra McMillan, Ambuj Tewari:
Online Learning via the Differential Privacy Lens. NeurIPS 2019: 8892-8902 - [c3]Clément L. Canonne, Gautam Kamath, Audra McMillan, Adam D. Smith, Jonathan R. Ullman:
The structure of optimal private tests for simple hypotheses. STOC 2019: 310-321 - [i6]Clément L. Canonne, Gautam Kamath, Audra McMillan, Jonathan R. Ullman, Lydia Zakynthinou:
Private Identity Testing for High-Dimensional Distributions. CoRR abs/1905.11947 (2019) - 2018
- [c2]Anna C. Gilbert, Audra McMillan:
Property Testing For Differential Privacy. Allerton 2018: 249-258 - [c1]Audra McMillan, Anna C. Gilbert:
Local differential privacy for physical sensor data and sparse recovery. CISS 2018: 1-6 - [i5]Anna C. Gilbert, Audra McMillan:
Property Testing for Differential Privacy. CoRR abs/1806.06427 (2018) - [i4]Clément L. Canonne, Gautam Kamath, Audra McMillan, Adam D. Smith, Jonathan R. Ullman:
The Structure of Optimal Private Tests for Simple Hypotheses. CoRR abs/1811.11148 (2018) - 2017
- [i3]Anna C. Gilbert, Audra McMillan:
Local Differential Privacy for Physical Sensor Data and Sparse Recovery. CoRR abs/1706.05916 (2017) - [i2]Jacob D. Abernethy, Chansoo Lee, Audra McMillan, Ambuj Tewari:
Online Learning via Differential Privacy. CoRR abs/1711.10019 (2017) - 2016
- [i1]Audra McMillan, Adam D. Smith:
When is Nontrivial Estimation Possible for Graphons and Stochastic Block Models? CoRR abs/1604.01871 (2016)
Coauthor Index
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last updated on 2024-12-23 19:35 CET by the dblp team
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