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We provide a differentially private algorithm for hypothesis selection.
Optimal schemes for discrete distribution estimation under locally differential privacy
Min Ye and Alexander Barg · 1905
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A refinement of an inequality of the brothers Markoff
Richard J. Duffin and Albert C. Schaeffer · 1941
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Randomized response: A survey technique for eliminating evasive answer bias
Stanley L. Warner · 1965
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Theory of Pattern Recognition
Vladimir Vapnik and Alexey Chervonenkis · 1974
Earlier work this paper cites.
Rates of convergence of minimum distance estimators and Kolmogorov’s entropy
Yannis G. Yatracos · 1985
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The growth of polynomials bounded at equally spaced points
Don Coppersmith and Theodore J. Rivlin · 1992
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On the learnability of discrete distributions
Michael Kearns, Yishay Mansour, Dana Ron, Ronitt Rubinfeld, Robert E. Schapire, and Linda Sellie · 1994
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Sharper bounds for Gaussian and empirical processes
Michel Talagrand · 1994
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Classification by polynomial surfaces
Martin Anthony · 1995
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A universally acceptable smoothing factor for kernel density estimation
Luc Devroye and Gábor Lugosi · 1996
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Nonasymptotic universal smoothing factors, kernel complexity and Yatracos classes
Luc Devroye and Gábor Lugosi · 1997
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Learning mixtures of Gaussians
Sanjoy Dasgupta · 1999
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A two-round variant of EM for Gaussian mixtures
Sanjoy Dasgupta and Leonard J. Schulman · 2000
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Learning mixtures of arbitrary Gaussians
Sanjeev Arora and Ravi Kannan · 2001
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Combinatorial methods in density estimation
Luc Devroye and Gábor Lugosi · 2001
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A spectral algorithm for learning mixtures of distributions
Santosh Vempala and Grant Wang · 2002
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Limiting privacy breaches in privacy preserving data mining
Alexandre Evfimievski, Johannes Gehrke, and Ramakrishnan Srikant · 2003
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On spectral learning of mixtures of distributions
Dimitris Achlioptas and Frank McSherry · 2005
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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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PAC learning axis-aligned mixtures of Gaussians with no separation assumption
Jon Feldman, Ryan O’Donnell, and Rocco A. Servedio · 2006
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Mechanism design via differential privacy
Frank McSherry and Kunal Talwar · 2007
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Smooth sensitivity and sampling in private data analysis
Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2007
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Beyond Gaussians: Spectral methods for learning mixtures of heavy-tailed distributions
Kamalika Chaudhuri and Satish Rao · 2008
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Learning mixtures of product distributions using correlations and independence
Kamalika Chaudhuri and Satish Rao · 2008
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Discretized multinomial distributions and Nash equilibria in anonymous games
Constantinos Daskalakis and Christos H. Papadimitriou · 2008
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Learning mixtures of product distributions over discrete domains
Jon Feldman, Ryan O’Donnell, and Rocco A. Servedio · 2008
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Density estimation in linear time
Satyaki Mahalanabis and Daniel Stefankovic · 2008
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Differential privacy and robust statistics
Cynthia Dwork and Jing Lei · 2009
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On oblivious PTAS’s for Nash equilibrium
Constantinos Daskalakis and Christos H. Papadimitriou · 2009
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Polynomial learning of distribution families
Mikhail Belkin and Kaushik Sinha · 2010
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Pan-private streaming algorithms
Cynthia Dwork, Moni Naor, Toniann Pitassi, Guy N. Rothblum, and Sergey Yekhanin · 2010
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On the geometry of differential privacy
Moritz Hardt and Kunal Talwar · 2010
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Clustering with spectral norm and the k-means algorithm
Amit Kumar and Ravindran Kannan · 2010
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Efficiently learning mixtures of two Gaussians
Adam Tauman Kalai, Ankur Moitra, and Gregory Valiant · 2010
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Settling the polynomial learnability of mixtures of Gaussians
Ankur Moitra and Gregory Valiant · 2010
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A CLT and tight lower bounds for estimating entropy
Gregory Valiant and Paul Valiant · 2010
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What can we learn privately?
Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2011
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Privacy-preserving statistical estimation with optimal convergence rates
Adam Smith · 2011
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Improved spectral-norm bounds for clustering
Pranjal Awasthi and Or Sheffet · 2012
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Learning k-modal distributions via testing
Constantinos Daskalakis, Ilias Diakonikolas, and Rocco A. Servedio · 2012
Cited alongside, same era.
Learning Poisson binomial distributions
Constantinos Daskalakis, Ilias Diakonikolas, and Rocco A. Servedio · 2012
Cited alongside, same era.
Learning sums of independent integer random variables
Constantinos Daskalakis, Ilias Diakonikolas, Ryan O’Donnell, Rocco A. Servedio, and Li Yang Tan · 2013
Cited alongside, same era.
Local privacy and statistical minimax rates
John C. Duchi, Michael I. Jordan, and Martin J. Wainwright · 2013
Cited alongside, same era.
Sample-optimal density estimation in nearly-linear time
Jayadev Acharya, Ilias Diakonikolas, Jerry Li, and Ludwig Schmidt · 2017
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Learning with privacy at scale
Differential Privacy Team, Apple · 2017
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The modernization of statistical disclosure limitation at the U.S. census bureau, 2017
Aref N. Dajani, Amy D. Lauger, Phyllis E. Singer, Daniel Kifer, Jerome P. Reiter, Ashwin Machanavajjhala, Simson L. Garfinkel, Scot A. Dahl, Matthew Graham, Vishesh Karwa, Hang Kim, Philip Lelerc, Ian M. Schmutte, William N. Sexton, Lars Vilhuber, and John M. Abowd · 2017
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Ten steps of EM suffice for mixtures of two Gaussians
Constantinos Daskalakis, Christos Tzamos, and Manolis Zampetakis · 2017
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Robust proper learning for mixtures of Gaussians via systems of polynomial inequalities
Jerry Li and Ludwig Schmidt · 2017
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Rényi differential privacy
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Learning mixtures of spherical Gaussians: Moment methods and spectral decompositions
Daniel Hsu and Sham M. Kakade · 2013
Cited alongside, same era.
Differentially private feature selection via stability arguments, and the robustness of the lasso
Abhradeep Guha Thakurta and Adam Smith · 2013
Cited alongside, same era.
The more, the merrier: the blessing of dimensionality for learning large Gaussian mixtures
Joseph Anderson, Mikhail Belkin, Navin Goyal, Luis Rademacher, and James R. Voss · 2014
Cited alongside, same era.
Sorting with adversarial comparators and application to density estimation
Jayadev Acharya, Ashkan Jafarpour, Alon Orlitsky, and Ananda Theertha Suresh · 2014
Cited alongside, same era.
Bounds on the sample complexity for private learning and private data release
Amos Beimel, Hai Brenner, Shiva Prasad Kasiviswanathan, and Kobbi Nissim · 2014
Cited alongside, same era.
Smoothed analysis of tensor decompositions
Aditya Bhaskara, Moses Charikar, Ankur Moitra, and Aravindan Vijayaraghavan · 2014
Cited alongside, same era.
Ilya Mironov · 2017
Later among the works it cites.
On learning mixtures of well-separated Gaussians
Oded Regev and Aravindan Vijayaraghavan · 2017
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Between pure and approximate differential privacy
Thomas Steinke and Jonathan Ullman · 2017
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The complexity of differential privacy
Salil Vadhan · 2017
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Nearly tight sample complexity bounds for learning mixtures of Gaussians via sample compression schemes
Hassan Ashtiani, Shai Ben-David, Nicholas Harvey, Christopher Liaw, Abbas Mehrabian, and Yaniv Plan · 2018
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Maximum selection and sorting with adversarial comparators
Jayadev Acharya, Moein Falahatgar, Ashkan Jafarpour, Alon Orlitsky, and Ananda Theertha Suresh · 2018
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Inspectre: Privately estimating the unseen
Jayadev Acharya, Gautam Kamath, Ziteng Sun, and Huanyu Zhang · 2018
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Composable and versatile privacy via truncated cdp
Mark Bun, Cynthia Dwork, Guy N. Rothblum, and Thomas Steinke · 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
Later among the works it cites.
Learning sums of independent random variables with sparse collective support
Anindya De, Philip M. Long, and Rocco A. Servedio · 2018
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The total variation distance between high-dimensional Gaussians
Luc Devroye, Abbas Mehrabian, and Tommy Reddad · 2018
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The right complexity measure in locally private estimation: It is not the Fisher information
John C. Duchi and Feng Ruan · 2018
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Mixture models, robustness, and sum of squares proofs
Samuel B. Hopkins and Jerry Li · 2018
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Robust moment estimation and improved clustering via sum of squares
Pravesh Kothari, Jacob Steinhardt, and David Steurer · 2018
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Finite sample differentially private confidence intervals
Vishesh Karwa and Salil Vadhan · 2018
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Private PAC learning implies finite Littlestone dimension
Noga Alon, Roi Livni, Maryanthe Malliaris, and Shay Moran · 2019
Closest in time.
Hadamard response: Estimating distributions privately, efficiently, and with little communication
Jayadev Acharya, Ziteng Sun, and Huanyu Zhang · 2019
Closest in time.
The optimal approximation factor in density estimation
Olivier Bousquet, Daniel M. Kane, and Shay Moran · 2019
Closest in time.
The structure of optimal private tests for simple hypotheses
Clément L. Canonne, Gautam Kamath, Audra McMillan, Adam Smith, and Jonathan Ullman · 2019
Closest in time.
Distributed differential privacy via shuffling
Albert Cheu, Adam Smith, Jonathan Ullman, David Zeber, and Maxim Zhilyaev · 2019
Closest in time.
The cost of privacy: Optimal rates of convergence for parameter estimation with differential privacy
T. Tony Cai, Yichen Wang, and Linjun Zhang · 2019
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Amplification by shuffling: From local to central differential privacy via anonymity
Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Abhradeep Thakurta · 2019
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Locally private confidence intervals: Z-test and tight confidence intervals
Marco Gaboardi, Ryan Rogers, and Or Sheffet · 2019
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Locally private Gaussian estimation
Matthew Joseph, Janardhan Kulkarni, Jieming Mao, and Zhiwei Steven Wu · 2019
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Privately learning high-dimensional distributions
Gautam Kamath, Jerry Li, Vikrant Singhal, and Jonathan Ullman · 2019
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Differentially private algorithms for learning mixtures of separated Gaussians
Gautam Kamath, Or Sheffet, Vikrant Singhal, and Jonathan Ullman · 2019
Closest in time.
The limits of pan privacy and shuffle privacy for learning and estimation
Albert Cheu and Jonathan Ullman · 2020
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Locally private hypothesis selection
Sivakanth Gopi, Gautam Kamath, Janardhan Kulkarni, Aleksandar Nikolov, Zhiwei Steven Wu, and Huanyu Zhang · 2020
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Private mean estimation of heavy-tailed distributions
Gautam Kamath, Vikrant Singhal, and Jonathan Ullman · 2020
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A primer on private statistics
Gautam Kamath and Jonathan Ullman · 2020
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Learning discrete distributions: User vs item-level privacy
Yuhan Liu, Ananda Theertha Suresh, Felix Yu, Sanjiv Kumar, and Michael Riley · 2020
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On the sample complexity of privately learning unbounded high-dimensional gaussians
Ishaq Aden-Ali, Hassan Ashtiani, and Gautam Kamath · 2021
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