March Boedihardjo

18 papers A* 1A 1Journal 15Unranked 1

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	On Extended Concentration Inequalities for Fast JL Embeddings of Infinite Sets. CoRR Edem Boahen, March Boedihardjo, Rafael Chiclana, Mark A. Iwen
2025	A*	`conf`	Statistical and Computational Guarantees of Kernel Max-Sliced Wasserstein Distances. ICML Jie Wang, March Boedihardjo, Yao Xie
2024	A	`conf`	Certified private data release for sparse Lipschitz functions. AISTATS Konstantin Donhauser, Johan Lokna, Amartya Sanyal, March Boedihardjo, Robert Hönig, Fanny Yang
2024	J	`jnl`	Covariance's Loss is Privacy's Gain: Computationally Efficient, Private and Accurate Synthetic Data. Found. Comput. Math. March Boedihardjo, Thomas Strohmer, Roman Vershynin
2024	J	`jnl`	Metric geometry of the privacy-utility tradeoff. CoRR March Boedihardjo, Thomas Strohmer, Roman Vershynin
2024	J	`jnl`	Statistical and Computational Guarantees of Kernel Max-Sliced Wasserstein Distances. CoRR Jie Wang, March Boedihardjo, Yao Xie
2023	J	`jnl`	Covariance loss, Szemeredi regularity, and differential privacy. CoRR March Boedihardjo, Thomas Strohmer, Roman Vershynin
2023	J	`jnl`	Privacy of Synthetic Data: A Statistical Framework. IEEE Trans. Inf. Theory March Boedihardjo, Thomas Strohmer, Roman Vershynin
2023	J	`jnl`	Sample-efficient private data release for Lipschitz functions under sparsity assumptions. CoRR Konstantin Donhauser, Johan Lokna, Amartya Sanyal, March Boedihardjo, Robert Hönig, Fanny Yang
2022	J	`jnl`	Private Sampling: A Noiseless Approach for Generating Differentially Private Synthetic Data. SIAM J. Math. Data Sci. March Boedihardjo, Thomas Strohmer, Roman Vershynin
2022	J	`jnl`	Private measures, random walks, and synthetic data. CoRR March Boedihardjo, Thomas Strohmer, Roman Vershynin
2021	J	`jnl`	A Performance Guarantee for Spectral Clustering. SIAM J. Math. Data Sci. March Boedihardjo, Shaofeng Deng, Thomas Strohmer
2021	J	`jnl`	Covariance's Loss is Privacy's Gain: Computationally Efficient, Private and Accurate Synthetic Data. CoRR March Boedihardjo, Thomas Strohmer, Roman Vershynin
2021	J	`jnl`	Privacy of synthetic data: a statistical framework. CoRR March Boedihardjo, Thomas Strohmer, Roman Vershynin
2021	J	`jnl`	Private sampling: a noiseless approach for generating differentially private synthetic data. CoRR March Boedihardjo, Thomas Strohmer, Roman Vershynin
2020	J	`jnl`	A Performance Guarantee for Spectral Clustering. CoRR March Boedihardjo, Shaofeng Deng, Thomas Strohmer
2020	—	`conf`	DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM. MSML Bao Wang, Quanquan Gu, March Boedihardjo, Lingxiao Wang, Farzin Barekat, Stanley J. Osher
2019	J	`jnl`	DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM. CoRR Bao Wang, Quanquan Gu, March Boedihardjo, Farzin Barekat, Stanley J. Osher