Audra McMillan

34 papers A* 9Misc 1Journal 20Unranked 4

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Local Pan-Privacy for Federated Analytics. CoRR Vitaly Feldman, Audra McMillan, Guy N. Rothblum, Kunal Talwar
2025	A*	`conf`	Local Pan-privacy for Federated Analytics. ICML Vitaly Feldman, Audra McMillan, Guy N. Rothblum, Kunal Talwar
2025	—	`conf`	Private Selection with Heterogeneous Sensitivities. SaTML Daniela Antonova, Allegra Laro, Audra McMillan, Lorenz Wolf
2025	J	`jnl`	Private Selection with Heterogeneous Sensitivities. CoRR Daniela Antonova, Allegra Laro, Audra McMillan, Lorenz Wolf
2024	—	`conf`	Differentially Private Heavy Hitter Detection using Federated Analytics. SaTML Karan N. Chadha, Junye Chen, John C. Duchi, Vitaly Feldman, Hanieh Hashemi, Omid Javidbakht, Audra McMillan, Kunal Talwar
2024	A*	`conf`	Instance-Optimal Private Density Estimation in the Wasserstein Distance. NeurIPS Vitaly Feldman, Audra McMillan, Satchit Sivakumar, Kunal Talwar
2024	J	`jnl`	Instance-Optimal Private Density Estimation in the Wasserstein Distance. CoRR Vitaly Feldman, Audra McMillan, Satchit Sivakumar, Kunal Talwar
2024	A*	`conf`	Samplable Anonymous Aggregation for Private Federated Data Analysis. CCS 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
2023	J	`jnl`	Differentially Private Heavy Hitter Detection using Federated Analytics. CoRR Karan N. Chadha, Junye Chen, John C. Duchi, Vitaly Feldman, Hanieh Hashemi, Omid Javidbakht, Audra McMillan, Kunal Talwar
2023	J	`jnl`	Mean Estimation with User-level Privacy under Data Heterogeneity. CoRR Rachel Cummings, Vitaly Feldman, Audra McMillan, Kunal Talwar
2023	J	`jnl`	Samplable Anonymous Aggregation for Private Federated Data Analysis. CoRR 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
2023	A*	`conf`	Stronger Privacy Amplification by Shuffling for Renyi and Approximate Differential Privacy. SODA Vitaly Feldman, Audra McMillan, Kunal Talwar
2022	—	`conf`	Controlling Privacy Loss in Sampling Schemes: An Analysis of Stratified and Cluster Sampling. FORC Mark Bun, Jörg Drechsler, Marco Gaboardi, Audra McMillan, Jayshree Sarathy
2022	J	`jnl`	Differentially Private Simple Linear Regression. Proc. Priv. Enhancing Technol. Daniel Alabi, Audra McMillan, Jayshree Sarathy, Adam D. Smith, Salil P. Vadhan
2022	J	`jnl`	Instance-Optimal Differentially Private Estimation. CoRR Audra McMillan, Adam D. Smith, Jonathan R. Ullman
2022	A*	`conf`	Mean Estimation with User-level Privacy under Data Heterogeneity. NeurIPS Rachel Cummings, Vitaly Feldman, Audra McMillan, Kunal Talwar
2022	J	`jnl`	Private Federated Statistics in an Interactive Setting. CoRR 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
2022	J	`jnl`	Stronger Privacy Amplification by Shuffling for Rényi and Approximate Differential Privacy. CoRR Vitaly Feldman, Audra McMillan, Kunal Talwar
2021	A*	`conf`	Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling. FOCS Vitaly Feldman, Audra McMillan, Kunal Talwar
2021	J	`jnl`	Non-parametric Differentially Private Confidence Intervals for the Median. CoRR Jörg Drechsler, Ira Globus-Harris, Audra McMillan, Jayshree Sarathy, Adam D. Smith
2020	J	`jnl`	Controlling Privacy Loss in Survey Sampling (Working Paper). CoRR Mark Bun, Jörg Drechsler, Marco Gaboardi, Audra McMillan
2020	J	`jnl`	Differentially Private Simple Linear Regression. CoRR Daniel Alabi, Audra McMillan, Jayshree Sarathy, Adam D. Smith, Salil P. Vadhan
2020	J	`jnl`	Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling. CoRR Vitaly Feldman, Audra McMillan, Kunal Talwar
2020	A*	`conf`	Private Identity Testing for High-Dimensional Distributions. NeurIPS Clément L. Canonne, Gautam Kamath, Audra McMillan, Jonathan R. Ullman, Lydia Zakynthinou
2019	A*	`conf`	Online Learning via the Differential Privacy Lens. NeurIPS Jacob D. Abernethy, Young Hun Jung, Chansoo Lee, Audra McMillan, Ambuj Tewari
2019	J	`jnl`	Private Identity Testing for High-Dimensional Distributions. CoRR Clément L. Canonne, Gautam Kamath, Audra McMillan, Jonathan R. Ullman, Lydia Zakynthinou
2019	A*	`conf`	The structure of optimal private tests for simple hypotheses. STOC Clément L. Canonne, Gautam Kamath, Audra McMillan, Adam D. Smith, Jonathan R. Ullman
2018	Misc	`conf`	Local differential privacy for physical sensor data and sparse recovery. CISS Audra McMillan, Anna C. Gilbert
2018	—	`conf`	Property Testing For Differential Privacy. Allerton Anna C. Gilbert, Audra McMillan
2018	J	`jnl`	Property Testing for Differential Privacy. CoRR Anna C. Gilbert, Audra McMillan
2018	J	`jnl`	The Structure of Optimal Private Tests for Simple Hypotheses. CoRR Clément L. Canonne, Gautam Kamath, Audra McMillan, Adam D. Smith, Jonathan R. Ullman
2017	J	`jnl`	Local Differential Privacy for Physical Sensor Data and Sparse Recovery. CoRR Anna C. Gilbert, Audra McMillan
2017	J	`jnl`	Online Learning via Differential Privacy. CoRR Jacob D. Abernethy, Chansoo Lee, Audra McMillan, Ambuj Tewari
2016	J	`jnl`	When is Nontrivial Estimation Possible for Graphons and Stochastic Block Models? CoRR Audra McMillan, Adam D. Smith