March Boedihardjo

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 16

Top Topics

Top Venues

SIAM J. Math. Data Sci.

IEEE Trans. Inf. Theory

Publications

March Boedihardjo, Thomas Strohmer, Roman Vershynin
Metric geometry of the privacy-utility tradeoff. CoRR (2024)
Jie Wang, March Boedihardjo, Yao Xie
Statistical and Computational Guarantees of Kernel Max-Sliced Wasserstein Distances. CoRR (2024)
Konstantin Donhauser, Johan Lokna, Amartya Sanyal, March Boedihardjo, Robert Hönig, Fanny Yang
Certified private data release for sparse Lipschitz functions. AISTATS (2024)
March Boedihardjo, Thomas Strohmer, Roman Vershynin
Covariance's Loss is Privacy's Gain: Computationally Efficient, Private and Accurate Synthetic Data. Found. Comput. Math. 24 (1) (2024)
Konstantin Donhauser, Johan Lokna, Amartya Sanyal, March Boedihardjo, Robert Hönig, Fanny Yang
Sample-efficient private data release for Lipschitz functions under sparsity assumptions. CoRR (2023)
March Boedihardjo, Thomas Strohmer, Roman Vershynin
Covariance loss, Szemeredi regularity, and differential privacy. CoRR (2023)
March Boedihardjo, Thomas Strohmer, Roman Vershynin
Privacy of Synthetic Data: A Statistical Framework. IEEE Trans. Inf. Theory 69 (1) (2023)
March Boedihardjo, Thomas Strohmer, Roman Vershynin
Private Sampling: A Noiseless Approach for Generating Differentially Private Synthetic Data. SIAM J. Math. Data Sci. 4 (3) (2022)
March Boedihardjo, Thomas Strohmer, Roman Vershynin
Private measures, random walks, and synthetic data. CoRR (2022)
March Boedihardjo, Shaofeng Deng, Thomas Strohmer
A Performance Guarantee for Spectral Clustering. SIAM J. Math. Data Sci. 3 (1) (2021)
March Boedihardjo, Thomas Strohmer, Roman Vershynin
Privacy of synthetic data: a statistical framework. CoRR (2021)
March Boedihardjo, Thomas Strohmer, Roman Vershynin
Private sampling: a noiseless approach for generating differentially private synthetic data. CoRR (2021)
March Boedihardjo, Thomas Strohmer, Roman Vershynin
Covariance's Loss is Privacy's Gain: Computationally Efficient, Private and Accurate Synthetic Data. CoRR (2021)
March Boedihardjo, Shaofeng Deng, Thomas Strohmer
A Performance Guarantee for Spectral Clustering. CoRR (2020)
Bao Wang, Quanquan Gu, March Boedihardjo, Lingxiao Wang, Farzin Barekat, Stanley J. Osher
DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM. MSML (2020)
Bao Wang, Quanquan Gu, March Boedihardjo, Farzin Barekat, Stanley J. Osher
DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM. CoRR (2019)