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ORCIDMarch Boedihardjo
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2020 – today
- 2024
[j4]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): 179-226 (2024)- [c2]Konstantin Donhauser, Johan Lokna, Amartya Sanyal, March Boedihardjo, Robert Hönig, Fanny Yang:
Certified private data release for sparse Lipschitz functions. AISTATS 2024: 1396-1404 - [i10]March Boedihardjo, Thomas Strohmer, Roman Vershynin:
Metric geometry of the privacy-utility tradeoff. CoRR abs/2405.00329 (2024) - [i9]Jie Wang, March Boedihardjo, Yao Xie:
Statistical and Computational Guarantees of Kernel Max-Sliced Wasserstein Distances. CoRR abs/2405.15441 (2024) - 2023
- [j3]March Boedihardjo, Thomas Strohmer, Roman Vershynin:
Privacy of Synthetic Data: A Statistical Framework. IEEE Trans. Inf. Theory 69(1): 520-527 (2023) - [i8]March Boedihardjo, Thomas Strohmer, Roman Vershynin:
Covariance loss, Szemeredi regularity, and differential privacy. CoRR abs/2301.02705 (2023) - [i7]Konstantin Donhauser, Johan Lokna, Amartya Sanyal, March Boedihardjo, Robert Hönig, Fanny Yang:
Sample-efficient private data release for Lipschitz functions under sparsity assumptions. CoRR abs/2302.09680 (2023) - 2022
- [j2]March Boedihardjo, Thomas Strohmer, Roman Vershynin:
Private Sampling: A Noiseless Approach for Generating Differentially Private Synthetic Data. SIAM J. Math. Data Sci. 4(3): 1082-1115 (2022) - [i6]March Boedihardjo, Thomas Strohmer, Roman Vershynin:
Private measures, random walks, and synthetic data. CoRR abs/2204.09167 (2022) - 2021
- [j1]March Boedihardjo, Shaofeng Deng, Thomas Strohmer:
A Performance Guarantee for Spectral Clustering. SIAM J. Math. Data Sci. 3(1): 369-387 (2021) - [i5]March Boedihardjo, Thomas Strohmer, Roman Vershynin:
Covariance's Loss is Privacy's Gain: Computationally Efficient, Private and Accurate Synthetic Data. CoRR abs/2107.05824 (2021) - [i4]March Boedihardjo, Thomas Strohmer, Roman Vershynin:
Privacy of synthetic data: a statistical framework. CoRR abs/2109.01748 (2021) - [i3]March Boedihardjo, Thomas Strohmer, Roman Vershynin:
Private sampling: a noiseless approach for generating differentially private synthetic data. CoRR abs/2109.14839 (2021) - 2020
- [c1]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: 328-351 - [i2]March Boedihardjo, Shaofeng Deng, Thomas Strohmer:
A Performance Guarantee for Spectral Clustering. CoRR abs/2007.05627 (2020)
2010 – 2019
- 2019
- [i1]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 abs/1906.12056 (2019)
Coauthor Index
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