Gaboardi, Marco

14 publications

JMLR 2023 Generalized Linear Models in Non-Interactive Local Differential Privacy with Public Data Di Wang, Lijie Hu, Huanyu Zhang, Marco Gaboardi, Jinhui Xu

NeurIPS 2021 Covariance-Aware Private Mean Estimation Without Private Covariance Estimation Gavin Brown, Marco Gaboardi, Adam Smith, Jonathan Ullman, Lydia Zakynthinou

ALT 2021 Estimating Smooth GLM in Non-Interactive Local Differential Privacy Model with Public Unlabeled Data Di Wang, Huangyu Zhang, Marco Gaboardi, Jinhui Xu

NeurIPS 2021 Multiclass Versus Binary Differentially Private PAC Learning Satchit Sivakumar, Mark Bun, Marco Gaboardi

JMLR 2020 Empirical Risk Minimization in the Non-Interactive Local Model of Differential Privacy Di Wang, Marco Gaboardi, Adam Smith, Jinhui Xu

AISTATS 2020 Hypothesis Testing Interpretations and Renyi Differential Privacy Borja Balle, Gilles Barthe, Marco Gaboardi, Justin Hsu, Tetsuya Sato

NeurIPS 2019 Facility Location Problem in Differential Privacy Model Revisited Yunus Esencayi, Marco Gaboardi, Shi Li, Di Wang

AISTATS 2019 Locally Private Mean Estimation: $z$-Test and Tight Confidence Intervals Marco Gaboardi, Ryan Rogers, Or Sheffet

NeurIPS 2019 Privacy Amplification by Mixing and Diffusion Mechanisms Borja Balle, Gilles Barthe, Marco Gaboardi, Joseph Geumlek

NeurIPS 2018 Empirical Risk Minimization in Non-Interactive Local Differential Privacy Revisited Di Wang, Marco Gaboardi, Jinhui Xu

ICML 2018 Local Private Hypothesis Testing: Chi-Square Tests Marco Gaboardi, Ryan Rogers

NeurIPS 2018 Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences Borja Balle, Gilles Barthe, Marco Gaboardi

ICML 2016 Differentially Private Chi-Squared Hypothesis Testing: Goodness of Fit and Independence Testing Marco Gaboardi, Hyun Lim, Ryan Rogers, Salil Vadhan

ICML 2014 Dual Query: Practical Private Query Release for High Dimensional Data Marco Gaboardi, Emilio Jesus Gallego Arias, Justin Hsu, Aaron Roth, Zhiwei Steven Wu