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In statistical learning and analysis from shared data, which is increasingly widely adopted in platforms such as federated learning and meta-learning, there are two major concerns: privacy and robustness.
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A survey of sampling from contaminated distributions
John W Tukey · 1960
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Robust Estimation of a Location Parameter
Peter J. Huber · 1964
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The complexity and approximability of finding maximum feasible subsystems of linear relations
Edoardo Amaldi and Viggo Kann · 1995
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Practical privacy: the sulq framework
Avrim Blum, Cynthia Dwork, Frank McSherry, and Kobbi Nissim · 2005
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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Mechanism design via differential privacy
Frank McSherry and Kunal Talwar · 2007
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Combinatorial methods in density estimation
Luc Devroye and Gábor Lugosi · 2012
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Terence Tao · 2012
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A near-optimal algorithm for differentially-private principal components
Kamalika Chaudhuri, Anand D Sarwate, and Kaushik Sinha · 2013
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The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
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Analyze gauss: optimal bounds for privacy-preserving principal component analysis
Cynthia Dwork, Kunal Talwar, Abhradeep Thakurta, and Li Zhang · 2014
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Rappor: Randomized aggregatable privacy-preserving ordinal response
Úlfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova · 2014
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Spectral sparsification and regret minimization beyond matrix multiplicative updates
Zeyuan Allen-Zhu, Zhenyu Liao, and Lorenzo Orecchia · 2015
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Robust regression via hard thresholding
Kush Bhatia, Prateek Jain, and Purushottam Kar · 2015
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The composition theorem for differential privacy
Peter Kairouz, Sewoong Oh, and Pramod Viswanath · 2015
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Is feature selection secure against training data poisoning?
Huang Xiao, Battista Biggio, Gavin Brown, Giorgio Fumera, Claudia Eckert, and Fabio Roli · 2015
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Building a rappor with the unknown: Privacy-preserving learning of associations and data dictionaries
Giulia Fanti, Vasyl Pihur, and Úlfar Erlingsson · 2016
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Agnostic estimation of mean and covariance
Kevin A Lai, Anup B Rao, and Santosh Vempala · 2016
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Analysis of a privacy-preserving pca algorithm using random matrix theory
Lu Wei, Anand D Sarwate, Jukka Corander, Alfred Hero, and Vahid Tarokh · 2016
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Computationally efficient robust sparse estimation in high dimensions
S. Balakrishnan, S. S. Du, J. Li, and A. Singh · 2017
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Consistent robust regression
K. Bhatia, P. Jain, P. Kamalaruban, and P. Kar · 2017
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Learning from untrusted data
Moses Charikar, Jacob Steinhardt, and Gregory Valiant · 2017
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Targeted backdoor attacks on deep learning systems using data poisoning
Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, and Dawn Song · 2017
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Being Robust (in High Dimensions) Can Be Practical
Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, and Alistair Stewart · 2017
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Statistical query lower bounds for robust estimation of high-dimensional gaussians and gaussian mixtures
Ilias Diakonikolas, Daniel M Kane, and Alistair Stewart · 2017
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Finite sample differentially private confidence intervals
Vishesh Karwa and Salil Vadhan · 2017
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Fast implementation of the tukey depth
Xiaohui Liu · 2017
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Privacy loss in apple’s implementation of differential privacy on macos 10.12
Jun Tang, Aleksandra Korolova, Xiaolong Bai, Xueqiang Wang, and Xiaofeng Wang · 2017
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The us census bureau adopts differential privacy
John M Abowd · 2018
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Robustly learning a gaussian: Getting optimal error, efficiently
Ilias Diakonikolas, Gautam Kamath, Daniel M Kane, Jerry Li, Ankur Moitra, and Alistair Stewart · 2018
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List-decodable robust mean estimation and learning mixtures of spherical gaussians
Ilias Diakonikolas, Daniel M Kane, and Alistair Stewart · 2018
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Mixture models, robustness, and sum of squares proofs
Privately learning high-dimensional distributions
Gautam Kamath, Jerry Li, Vikrant Singhal, and Jonathan Ullman · 2019
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List-decodable linear regression
Sushrut Karmalkar, Adam Klivans, and Pravesh Kothari · 2019
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Compressed sensing with adversarial sparse noise via l1 regression
Sushrut Karmalkar and Eric Price · 2019
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CSE 599-M, Lecture Notes: Robustness in Machine Learning , 2019
Jerry Li · 2019
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Fast computation of tukey trimmed regions and median in dimension p> 2
Xiaohui Liu, Karl Mosler, and Pavlo Mozharovskyi · 2019
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Sub-gaussian estimators of the mean of a random vector
Gábor Lugosi, Shahar Mendelson, et al · 2019
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Samuel B Hopkins and Jerry Li · 2018
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Efficient algorithms for outlier-robust regression
Adam Klivans, Pravesh K Kothari, and Raghu Meka · 2018
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Robust moment estimation and improved clustering via sum of squares
Pravesh K Kothari, Jacob Steinhardt, and David Steurer · 2018
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High dimensional robust sparse regression
Liu Liu, Yanyao Shen, Tianyang Li, and Constantine Caramanis · 2018
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Robust estimation via robust gradient estimation
A. Prasad, A. S. Suggala, S. Balakrishnan, and P. Ravikumar · 2018
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Resilience: A criterion for learning in the presence of arbitrary outliers
Jacob Steinhardt, Moses Charikar, and Gregory Valiant · 2018
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Private center points and learning of halfspaces
Amos Beimel, Shay Moran, Kobbi Nissim, and Uri Stemmer · 2019
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Globally-convergent iteratively reweighted least squares for robust regression problems
Bhaskar Mukhoty, Govind Gopakumar, Prateek Jain, and Purushottam Kar · 2019
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High-dimensional statistics: A non-asymptotic viewpoint
Martin J Wainwright · 2019
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Generalized resilience and robust statistics
Banghua Zhu, Jiantao Jiao, and Jacob Steinhardt · 2019
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On the sample complexity of privately learning unbounded high-dimensional gaussians
Ishaq Aden-Ali, Hassan Ashtiani, and Gautam Kamath · 2020
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List-decodable subspace recovery via sum-of-squares
Ainesh Bakshi and Pravesh Kothari · 2020
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Coinpress: Practical private mean and covariance estimation
Sourav Biswas, Yihe Dong, Gautam Kamath, and Jonathan Ullman · 2020
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List decodable mean estimation in nearly linear time
Yeshwanth Cherapanamjeri, Sidhanth Mohanty, and Morris Yau · 2020
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Designing differentially private estimators in high dimensions
Aditya Dhar and Jason Huang · 2020
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Robustly learning any clusterable mixture of gaussians
Ilias Diakonikolas, Samuel B Hopkins, Daniel Kane, and Sushrut Karmalkar · 2020
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Robust regression via mutivariate regression depth
Chao Gao et al · 2020
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Robust and heavy-tailed mean estimation made simple, via regret minimization
Sam Hopkins, Jerry Li, and Fred Zhang · 2020
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Mean estimation with sub-gaussian rates in polynomial time
Samuel B Hopkins · 2020
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Robust sub-gaussian principal component analysis and width-independent schatten packing
Arun Jambulapati, Jerry Li, and Kevin Tian · 2020
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Differentially private algorithms for learning mixtures of separated gaussians
Gautam Kamath, Or Sheffet, Vikrant Singhal, and Jonathan Ullman · 2020
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Private mean estimation of heavy-tailed distributions
Gautam Kamath, Vikrant Singhal, and Jonathan Ullman · 2020
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Privately learning thresholds: Closing the exponential gap
Haim Kaplan, Katrina Ligett, Yishay Mansour, Moni Naor, and Uri Stemmer · 2020
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Robust meta-learning for mixed linear regression with small batches
Weihao Kong, Raghav Somani, Sham Kakade, and Sewoong Oh · 2020
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Robust gaussian covariance estimation in nearly-matrix multiplication time
Jerry Li and Guanghao Ye · 2020
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List decodable learning via sum of squares
Prasad Raghavendra and Morris Yau · 2020
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Privately learning markov random fields
Huanyu Zhang, Gautam Kamath, Janardhan Kulkarni, and Zhiwei Steven Wu · 2020
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When does the tukey median work?
Banghua Zhu, Jiantao Jiao, and Jacob Steinhardt · 2020
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