When is non-trivial estimation possible for graphons and stochastic block models? von McMillan, Audra, Smith, Adam

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Differentially Private Simple Linear Regression von Alabi, Daniel, McMillan, Audra, Sarathy, Jayshree, Smith, Adam, Vadhan, Salil

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Nonparametric Differentially Private Confidence Intervals for the Median von Drechsler, Jörg, Globus-Harris, Ira, Mcmillan, Audra, Sarathy, Jayshree, Smith, Adam

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Total positivity of a shuffle matrix von McMillan, Audra

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Private Selection with Heterogeneous Sensitivities von Antonova, Daniela, Laro, Allegra, McMillan, Audra, Wolf, Lorenz

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Instance-Optimal Private Density Estimation in the Wasserstein Distance von Feldman, Vitaly, McMillan, Audra, Sivakumar, Satchit, Talwar, Kunal

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Instance-Optimal Differentially Private Estimation von McMillan, Audra, Smith, Adam, Ullman, Jon

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Stronger Privacy Amplification by Shuffling for R\'enyi and Approximate Differential Privacy von Feldman, Vitaly, McMillan, Audra, Talwar, Kunal

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Mean Estimation with User-level Privacy under Data Heterogeneity von Cummings, Rachel, Feldman, Vitaly, McMillan, Audra, Talwar, Kunal

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Property Testing for Differential Privacy von Gilbert, Anna, McMillan, Audra

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Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling von Feldman, Vitaly, McMillan, Audra, Talwar, Kunal

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Differentially Private Heavy Hitter Detection using Federated Analytics von Chadha, Karan, Chen, Junye, Duchi, John, Feldman, Vitaly, Hashemi, Hanieh, Javidbakht, Omid, McMillan, Audra, Talwar, Kunal

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Stronger Privacy Amplification by Shuffling for Rényi and Approximate Differential Privacy von Feldman, Vitaly, McMillan, Audra, Talwar, Kunal

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When is Nontrivial Estimation Possible for Graphons and Stochastic Block Models? von McMillan, Audra, Smith, Adam

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Local Differential Privacy for Physical Sensor Data and Sparse Recovery von Gilbert, Anna C, McMillan, Audra

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Controlling Privacy Loss in Sampling Schemes: an Analysis of Stratified and Cluster Sampling von Bun, Mark, Drechsler, Jörg, Gaboardi, Marco, McMillan, Audra, Sarathy, Jayshree

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Differentially Private Simple Linear Regression von Alabi, Daniel, McMillan, Audra, Sarathy, Jayshree, Smith, Adam, Vadhan, Salil

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Instance-Optimal Private Density Estimation in the Wasserstein Distance von Feldman, Vitaly, McMillan, Audra, Sivakumar, Satchit, Talwar, Kunal

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Instance-Optimal Differentially Private Estimation von McMillan, Audra, Smith, Adam, Ullman, Jon

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Property Testing for Differential Privacy von Gilbert, Anna, McMillan, Audra

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