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2002.09464
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Private Mean Estimation of Heavy-Tailed Distributions
Annual Conference Computational Learning Theory (COLT), 2020
21 February 2020
Gautam Kamath
Vikrant Singhal
Jonathan R. Ullman
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Papers citing
"Private Mean Estimation of Heavy-Tailed Distributions"
50 / 77 papers shown
Privately Estimating Black-Box Statistics
Günter F. Steinke
Thomas Steinke
125
0
0
30 Sep 2025
Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition
Difei Xu
Meng Ding
Zihang Xiang
Jinhui Xu
Haiyan Zhao
244
2
0
04 Sep 2025
Differentially Private Sparse Linear Regression with Heavy-tailed Responses
Xizhi Tian
Meng Ding
Touming Tao
Zihang Xiang
Di Wang
227
2
0
07 Jun 2025
Towards hyperparameter-free optimization with differential privacy
International Conference on Learning Representations (ICLR), 2025
Zhiqi Bu
Ruixuan Liu
339
7
0
02 Mar 2025
Learning with Differentially Private (Sliced) Wasserstein Gradients
David Rodríguez-Vítores
Clément Lalanne
Jean-Michel Loubes
FedML
550
0
0
03 Feb 2025
Differentially Private Multi-Sampling from Distributions
International Conference on Algorithmic Learning Theory (ALT), 2024
Albert Cheu
Debanuj Nayak
245
2
0
13 Dec 2024
Statistical-Computational Trade-offs for Density Estimation
Neural Information Processing Systems (NeurIPS), 2024
Anders Aamand
Alexandr Andoni
Justin Y. Chen
Piotr Indyk
Shyam Narayanan
Sandeep Silwal
Haike Xu
226
2
0
30 Oct 2024
Privately Learning Smooth Distributions on the Hypercube by Projections
International Conference on Machine Learning (ICML), 2024
Clément Lalanne
Sébastien Gadat
404
1
0
16 Sep 2024
Differential Private Stochastic Optimization with Heavy-tailed Data: Towards Optimal Rates
AAAI Conference on Artificial Intelligence (AAAI), 2024
Puning Zhao
Yan Han
Zhe Liu
Chong Wang
Rongfei Fan
Qingming Li
228
1
0
19 Aug 2024
Private Means and the Curious Incident of the Free Lunch
Jack Fitzsimons
James Honaker
Michael Shoemate
Vikrant Singhal
441
3
0
19 Aug 2024
On Differentially Private U Statistics
Kamalika Chaudhuri
Po-Ling Loh
Shourya Pandey
Purnamrita Sarkar
FedML
230
2
0
06 Jul 2024
Distribution Learnability and Robustness
Shai Ben-David
Alex Bie
Gautam Kamath
Tosca Lechner
371
5
0
25 Jun 2024
Optimal Federated Learning for Nonparametric Regression with Heterogeneous Distributed Differential Privacy Constraints
T. T. Cai
Abhinav Chakraborty
Lasse Vuursteen
FedML
351
9
0
10 Jun 2024
Federated Nonparametric Hypothesis Testing with Differential Privacy Constraints: Optimal Rates and Adaptive Tests
T. T. Cai
Abhinav Chakraborty
Lasse Vuursteen
FedML
321
6
0
10 Jun 2024
A Huber Loss Minimization Approach to Mean Estimation under User-level Differential Privacy
Puning Zhao
Lifeng Lai
Li Shen
Qingming Li
Yan Han
Zhe Liu
307
13
0
22 May 2024
Near-Optimal differentially private low-rank trace regression with guaranteed private initialization
Mengyue Zha
283
0
0
24 Mar 2024
A Simple and Practical Method for Reducing the Disparate Impact of Differential Privacy
Lucas Rosenblatt
Julia Stoyanovich
Christopher Musco
230
5
0
18 Dec 2023
Mean estimation in the add-remove model of differential privacy
Alex Kulesza
A. Suresh
Yuyan Wang
261
10
0
11 Dec 2023
Sample-Optimal Locally Private Hypothesis Selection and the Provable Benefits of Interactivity
Annual Conference Computational Learning Theory (COLT), 2023
A. F. Pour
Hassan Ashtiani
S. Asoodeh
289
2
0
09 Dec 2023
Instance-Specific Asymmetric Sensitivity in Differential Privacy
Neural Information Processing Systems (NeurIPS), 2023
David Durfee
294
1
0
02 Nov 2023
Improved Analysis of Sparse Linear Regression in Local Differential Privacy Model
International Conference on Learning Representations (ICLR), 2023
Liyang Zhu
Meng Ding
Vaneet Aggarwal
Jinhui Xu
Haiyan Zhao
231
5
0
11 Oct 2023
Better and Simpler Lower Bounds for Differentially Private Statistical Estimation
IEEE Transactions on Information Theory (IEEE Trans. Inf. Theory), 2023
Shyam Narayanan
FedML
352
15
0
10 Oct 2023
Striking a Balance: An Optimal Mechanism Design for Heterogenous Differentially Private Data Acquisition for Logistic Regression
Ameya Anjarlekar
Rasoul Etesami
R. Srikant
332
4
0
19 Sep 2023
Private Federated Learning with Autotuned Compression
International Conference on Machine Learning (ICML), 2023
Enayat Ullah
Christopher A. Choquette-Choo
Peter Kairouz
Sewoong Oh
FedML
267
8
0
20 Jul 2023
Data Structures for Density Estimation
International Conference on Machine Learning (ICML), 2023
Anders Aamand
Alexandr Andoni
Justin Y. Chen
Piotr Indyk
Shyam Narayanan
Sandeep Silwal
212
6
0
20 Jun 2023
PLAN: Variance-Aware Private Mean Estimation
Proceedings on Privacy Enhancing Technologies (PoPETs), 2023
Martin Aumüller
C. Lebeda
Boel Nelson
Rasmus Pagh
FedML
293
6
0
14 Jun 2023
Differentially Private Episodic Reinforcement Learning with Heavy-tailed Rewards
International Conference on Machine Learning (ICML), 2023
Yulian Wu
Xingyu Zhou
Sayak Ray Chowdhury
Haiyan Zhao
423
4
0
01 Jun 2023
A Polynomial Time, Pure Differentially Private Estimator for Binary Product Distributions
International Conference on Algorithmic Learning Theory (ALT), 2023
Vikrant Singhal
627
10
0
13 Apr 2023
Score Attack: A Lower Bound Technique for Optimal Differentially Private Learning
Tianxi Cai
Yichen Wang
Linjun Zhang
282
24
0
13 Mar 2023
Subset-Based Instance Optimality in Private Estimation
International Conference on Machine Learning (ICML), 2023
Travis Dick
Alex Kulesza
Ziteng Sun
A. Suresh
382
10
0
01 Mar 2023
On Private and Robust Bandits
Neural Information Processing Systems (NeurIPS), 2023
Yulian Wu
Xingyu Zhou
Youming Tao
Haiyan Zhao
326
10
0
06 Feb 2023
From Robustness to Privacy and Back
International Conference on Machine Learning (ICML), 2023
Hilal Asi
Jonathan R. Ullman
Lydia Zakynthinou
306
39
0
03 Feb 2023
Continual Mean Estimation Under User-Level Privacy
IEEE Journal on Selected Areas in Information Theory (JSAIT), 2022
Anand George
Lekshmi Ramesh
A. V. Singh
Himanshu Tyagi
FedML
193
12
0
20 Dec 2022
Privately Estimating a Gaussian: Efficient, Robust and Optimal
Daniel Alabi
Pravesh Kothari
Pranay Tankala
Prayaag Venkat
Fred Zhang
420
0
0
15 Dec 2022
Robustness Implies Privacy in Statistical Estimation
Symposium on the Theory of Computing (STOC), 2022
Samuel B. Hopkins
Gautam Kamath
Mahbod Majid
Shyam Narayanan
667
65
0
09 Dec 2022
Privacy Induces Robustness: Information-Computation Gaps and Sparse Mean Estimation
Neural Information Processing Systems (NeurIPS), 2022
Kristian Georgiev
Samuel B. Hopkins
FedML
336
30
0
01 Nov 2022
Instance-Optimal Differentially Private Estimation
Audra McMillan
Adam D. Smith
Jonathan R. Ullman
242
8
0
28 Oct 2022
Differentially private multivariate medians
Kelly Ramsay
Aukosh Jagannath
Shojaéddin Chenouri
302
4
0
12 Oct 2022
On the Impossible Safety of Large AI Models
El-Mahdi El-Mhamdi
Sadegh Farhadkhani
R. Guerraoui
Nirupam Gupta
L. Hoang
Rafael Pinot
Sébastien Rouault
John Stephan
397
38
0
30 Sep 2022
Private Stochastic Optimization With Large Worst-Case Lipschitz Parameter: Optimal Rates for (Non-Smooth) Convex Losses and Extension to Non-Convex Losses
Journal of Privacy and Confidentiality (JPC), 2022
Andrew Lowy
Meisam Razaviyayn
465
13
0
15 Sep 2022
Archimedes Meets Privacy: On Privately Estimating Quantiles in High Dimensions Under Minimal Assumptions
Neural Information Processing Systems (NeurIPS), 2022
Omri Ben-Eliezer
Dan Mikulincer
Ilias Zadik
FedML
328
8
0
15 Aug 2022
Efficient Private SCO for Heavy-Tailed Data via Clipping
Machine-mediated learning (ML), 2022
Chenhan Jin
Kaiwen Zhou
Bo Han
Ming Yang
James Cheng
210
3
0
27 Jun 2022
Differentially Private Maximal Information Coefficients
International Conference on Machine Learning (ICML), 2022
John Lazarsfeld
Aaron Johnson
Emmanuel Adéníran
233
3
0
21 Jun 2022
Algorithms for bounding contribution for histogram estimation under user-level privacy
International Conference on Machine Learning (ICML), 2022
Yuhan Liu
A. Suresh
Wennan Zhu
Peter Kairouz
Marco Gruteser
208
13
0
07 Jun 2022
DP-PCA: Statistically Optimal and Differentially Private PCA
Neural Information Processing Systems (NeurIPS), 2022
Xiyang Liu
Weihao Kong
Prateek Jain
Sewoong Oh
415
32
0
27 May 2022
New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma
Neural Information Processing Systems (NeurIPS), 2022
Gautam Kamath
Argyris Mouzakis
Vikrant Singhal
FedML
534
36
0
17 May 2022
Private High-Dimensional Hypothesis Testing
Annual Conference Computational Learning Theory (COLT), 2022
Shyam Narayanan
FedML
431
17
0
03 Mar 2022
Nonparametric extensions of randomized response for private confidence sets
International Conference on Machine Learning (ICML), 2022
Ian Waudby-Smith
Zhiwei Steven Wu
Aaditya Ramdas
326
10
0
17 Feb 2022
Differentially Private
ℓ
1
\ell_1
ℓ
1
-norm Linear Regression with Heavy-tailed Data
International Symposium on Information Theory (ISIT), 2022
Haiyan Zhao
Jinhui Xu
234
10
0
10 Jan 2022
Optimal and Differentially Private Data Acquisition: Central and Local Mechanisms
ACM Conference on Economics and Computation (EC), 2022
Alireza Fallah
A. Makhdoumi
Azarakhsh Malekian
Asuman Ozdaglar
FedML
459
49
0
10 Jan 2022
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