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We consider the problem of learning Markov Random Fields (including the prototypical example, the Ising model) under the constraint of differential privacy.
Beitrag zur theorie des ferromagnetismus
Ernst Ising · 1925
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Approximating discrete probability distributions with dependence trees
C.K. Chow and C.N. Liu · 1968
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Markov random field image models and their applications to computer vision
Stuart Geman and Christine Graffigne · 1986
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Glenn Ellison · 1993
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Using Bayesian networks to analyze expression data
Nir Friedman, Michal Linial, Iftach Nachman, and Dana Pe’er · 2000
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Automatic identification of patients eligible for a pneumonia guideline: comparing the diagnostic accuracy of two decision support models
Charles Lagor, Dominik Aronsky, Marcelo Fiszman, and Peter J. Haug · 2001
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Cost-sensitive learning by cost-proportionate example weighting
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Inferring Phylogenies
Joseph Felsenstein · 2004
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Practical privacy: The SuLQ framework
Avrim Blum, Cynthia Dwork, Frank McSherry, and Kobbi Nissim · 2005
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Concentration Inequalities with Exchangeable Pairs
Sourav Chatterjee · 2005
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Learning factor graphs in polynomial time and sample complexity
Pieter Abbeel, Daphne Koller, and Andrew Y. Ng · 2006
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Consistent estimation of the basic neighborhood of Markov random fields
Imre Csiszár and Zsolt Talata · 2006
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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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Smooth sensitivity and sampling in private data analysis
Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2007
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Differential privacy and robust statistics
Cynthia Dwork and Jing Lei · 2009
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Releasing search queries and clicks privately
Aleksandra Korolova, Krishnaram Kenthapadi, Nina Mishra, and Alexandros Ntoulas · 2009
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Markov Chains and Mixing Times
David A. Levin, Yuval Peres, and Elizabeth L. Wilmer · 2009
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Coresets, sparse greedy approximation, and the frank-wolfe algorithm
Kenneth L Clarkson · 2010
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A multiplicative weights mechanism for privacy-preserving data analysis
Moritz Hardt and Guy N. Rothblum · 2010
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On the geometry of differential privacy
Moritz Hardt and Kunal Talwar · 2010
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The spread of innovations in social networks
Andrea Montanari and Amin Saberi · 2010
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High-dimensional Ising model selection using ℓ 1 \ell_{1} -regularized logistic regression
Pradeep Ravikumar, Martin J. Wainwright, and John D. Lafferty · 2010
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Evolutionary trees and the Ising model on the Bethe lattice: A proof of Steel’s conjecture
Constantinos Daskalakis, Elchanan Mossel, and Sébastien Roch · 2011
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On learning discrete graphical models using greedy methods
Ali Jalali, Christopher C. Johnson, and Pradeep K. Ravikumar · 2011
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On learning discrete graphical models using group-sparse regularization
Ali Jalali, Pradeep K. Ravikumar, Vishvas Vasuki, and Sujay Sanghavi · 2011
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Privacy-preserving statistical estimation with optimal convergence rates
Adam Smith · 2011
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Probability theory: The coupling method
Frank den Hollander · 2012
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Information-theoretic limits of selecting binary graphical models in high dimensions
Narayana P. Santhanam and Martin J. Wainwright · 2012
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Revisiting frank-wolfe: Projection-free sparse convex optimization
Martin Jaggi · 2013
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Bounds on the sample complexity for private learning and private data release
Amos Beimel, Hai Brenner, Shiva Prasad Kasiviswanathan, and Kobbi Nissim · 2014
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Structure learning of antiferromagnetic Ising models
Guy Bresler, David Gamarnik, and Devavrat Shah · 2014
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Information theoretic properties of Markov random fields, and their algorithmic applications
Linus Hamilton, Frederic Koehler, and Ankur Moitra · 2017
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Learning graphical models using multiplicative weights
Adam Klivans and Raghu Meka · 2017
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High dimensional statistics
Philippe Rigollet and Jan-Christian Hütter · 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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Inspectre: Privately estimating the unseen
Jayadev Acharya, Gautam Kamath, Ziteng Sun, and Huanyu Zhang · 2018
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Fingerprinting codes and the price of approximate differential privacy
Mark Bun, Jonathan Ullman, and Salil Vadhan · 2014
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RAPPOR: Randomized aggregatable privacy-preserving ordinal response
Úlfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova · 2014
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Dual query: Practical private query release for high dimensional data
Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu, Aaron Roth, and Zhiwei Steven Wu · 2014
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Private empirical risk minimization beyond the worst case: The effect of the constraint set geometry
Kunal Talwar, Abhradeep Thakurta, and Li Zhang · 2014
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Differentially private release and learning of threshold functions
Mark Bun, Kobbi Nissim, Uri Stemmer, and Salil Vadhan · 2015
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Efficiently learning Ising models on arbitrary graphs
Guy Bresler · 2015
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Testing Ising models
Constantinos Daskalakis, Nishanth Dikkala, and Gautam Kamath · 2018
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The minimax learning rate of normal and Ising undirected graphical models
Luc Devroye, Abbas Mehrabian, and Tommy Reddad · 2018
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Concentration inequalities for polynomials of contracting Ising models
Reza Gheissari, Eyal Lubetzky, and Yuval Peres · 2018
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Finite sample differentially private confidence intervals
Vishesh Karwa and Salil Vadhan · 2018
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Optimal structure and parameter learning of Ising models
Andrey Y. Lokhov, Marc Vuffray, Sidhant Misra, and Michael Chertkov · 2018
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Global testing against sparse alternatives under Ising models
Rajarshi Mukherjee, Sumit Mukherjee, and Ming Yuan · 2018
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Lower bounds for testing graphical models: Colorings and antiferromagnetic Ising models
Ivona Bezakova, Antonio Blanca, Zongchen Chen, Daniel Štefankovič, and Eric Vigoda · 2019
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A general asymptotic framework for distribution-free graph-based two-sample tests
Bhaswar B. Bhattacharya · 2019
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Private hypothesis selection
Mark Bun, Gautam Kamath, Thomas Steinke, and Zhiwei Steven Wu · 2019
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Data-dependent differentially private parameter learning for directed graphical models
Amrita Roy Chowdhury, Theodoros Rekatsinas, and Somesh Jha · 2019
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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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Testing Ising models
Constantinos Daskalakis, Nishanth Dikkala, and Gautam Kamath · 2019
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Privately learning high-dimensional distributions
Gautam Kamath, Jerry Li, Vikrant Singhal, and Jonathan Ullman · 2019
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Graphical-model based estimation and inference for differential privacy
Ryan McKenna, Daniel Sheldon, and Gerome Miklau · 2019
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New oracle efficient algorithms for private synthetic data release
Giuseppe Vietri, Grace Tian, Mark Bun, Thomas Steinke, and Zhiwei Steven Wu · 2019
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Sparse logistic regression learns all discrete pairwise graphical models
Shanshan Wu, Sujay Sanghavi, and Alexandros G. Dimakis · 2019
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Personal communication, 2020
Jayadev Acharya, Ziteng Sun, and Huanyu Zhang · 2020
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Differentially private assouad, fano, and le cam
Jayadev Acharya, Ziteng Sun, and Huanyu Zhang · 2020
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A primer on private statistics
Gautam Kamath and Jonathan Ullman · 2020
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