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We develop theory for using heuristics to solve computationally hard problems in differential privacy.
Exact identification of read-once formulas using fixed points of amplification functions
Sally A Goldman, Michael J Kearns, and Robert E Schapire · 1993
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An introduction to computational learning theory
Michael J Kearns and Umesh Vazirani · 1994
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Game theory, on-line prediction and boosting
Yoav Freund and Robert E. Schapire · 1996
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Uniform generation of np-witnesses using an np-oracle
Mihir Bellare, Oded Goldreich, and Erez Petrank · 2000
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Adaptive estimation of a quadratic functional by model selection
B. Laurent and P. Massart · 2000
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Error limiting reductions between classification tasks
Alina Beygelzimer, Varsha Dani, Thomas P. Hayes, John Langford, and Bianca Zadrozny · 2005
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Efficient algorithms for online decision problems
Adam Kalai and Santosh Vempala · 2005
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Our data, ourselves: Privacy via distributed noise generation
Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor · 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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Privacy, accuracy, and consistency too: a holistic solution to contingency table release
Boaz Barak, Kamalika Chaudhuri, Cynthia Dwork, Satyen Kale, Frank McSherry, and Kunal Talwar · 2007
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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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Robust reductions from ranking to classification
Maria-Florina Balcan, Nikhil Bansal, Alina Beygelzimer, Don Coppersmith, John Langford, and Gregory B. Sorkin · 2008
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On agnostic learning of parities, monomials, and halfspaces
Vitaly Feldman, Parikshit Gopalan, Subhash Khot, and Ashok Kumar Ponnuswami · 2009
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Boosting and differential privacy
Cynthia Dwork, Guy N. Rothblum, and Salil Vadhan · 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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The price of privately releasing contingency tables and the spectra of random matrices with correlated rows
Shiva Prasad Kasiviswanathan, Mark Rudelson, Adam Smith, and Jonathan Ullman · 2010
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Interactive privacy via the median mechanism
Aaron Roth and Tim Roughgarden · 2010
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Pcps and the hardness of generating private synthetic data
John Ullman and Salil P. Vadhan · 2010
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Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D Sarwate · 2011
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Hardness results for agnostically learning low-degree polynomial threshold functions
Ilias Diakonikolas, Ryan O’Donnell, Rocco A Servedio, and Yi Wu · 2011
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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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The multiplicative weights update method: a meta-algorithm and applications
Sanjeev Arora, Elad Hazan, and Satyen Kale · 2012
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Agnostic learning of monomials by halfspaces is hard
Vitaly Feldman, Venkatesan Guruswami, Prasad Raghavendra, and Yi Wu · 2012
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Iterative constructions and private data release
Anupam Gupta, Aaron Roth, and Jonathan Ullman · 2012
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Private data release via learning thresholds
Moritz Hardt, Guy N Rothblum, and Rocco A Servedio · 2012
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Oracle-based robust optimization via online learning
Aharon Ben-Tal, Elad Hazan, Tomer Koren, and Shie Mannor · 2015
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Preserving statistical validity in adaptive data analysis
Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Aaron Leon Roth · 2015
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Learning reductions that really work
Alina Beygelzimer, Hal Daumé III, John Langford, and Paul Mineiro · 2016
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Adaptive learning with robust generalization guarantees
Rachel Cummings, Katrina Ligett, Kobbi Nissim, Aaron Roth, and Zhiwei Steven Wu · 2016
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The computational power of optimization in online learning
Elad Hazan and Tomer Koren · 2016
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Semi-supervised knowledge transfer for deep learning from private training data
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Faster algorithms for privately releasing marginals
Justin Thaler, Jonathan Ullman, and Salil Vadhan · 2012
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A learning theory approach to noninteractive database privacy
Avrim Blum, Katrina Ligett, and Aaron Roth · 2013
Cited alongside, same era.
Concentration inequalities for sampling without replacement
R. Bardenet and O.-A. Maillard · 2013
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Privately releasing conjunctions and the statistical query barrier
Anupam Gupta, Moritz Hardt, Aaron Roth, and Jonathan Ullman · 2013
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Differential privacy for the analyst via private equilibrium computation
Justin Hsu, Aaron Roth, and Jonathan Ullman · 2013
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The geometry of differential privacy: the sparse and approximate cases
Aleksandar Nikolov, Kunal Talwar, and Li Zhang · 2013
Cited alongside, same era.
Nicolas Papernot, Martín Abadi, Ulfar Erlingsson, Ian Goodfellow, and Kunal Talwar · 2016
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Efficient algorithms for adversarial contextual learning
Vasilis Syrgkanis, Akshay Krishnamurthy, and Robert E. Schapire · 2016
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Answering nˆ2+o(1) counting queries with differential privacy is hard
Jonathan Ullman · 2016
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Supersparse linear integer models for optimized medical scoring systems
Berk Ustun and Cynthia Rudin · 2016
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Online learning via differential privacy
Jacob Abernethy, Chansoo Lee, Audra McMillan, and Ambuj Tewari · 2017
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Oracle-efficient online learning and auction design
Miroslav Dudík, Nika Haghtalab, Haipeng Luo, Robert E Schapire, Vasilis Syrgkanis, and Jennifer Wortman Vaughan · 2017
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Lecture 15: Algorithmic foundations of adaptive data analysis
Aaron Roth and Adam Smith · 2017
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A reductions approach to fair classification
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, and Hanna M. Wallach · 2018
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Unleashing linear optimizers for group-fair learning and optimization
Daniel Alabi, Nicole Immorlica, and Adam Kalai · 2018
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Private pac learning implies finite littlestone dimension
Noga Alon, Roi Livni, Maryanthe Malliaris, and Shay Moran · 2018
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Model-agnostic private learning via stability
Raef Bassily, Om Thakkar, and Abhradeep Thakurta · 2018
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Cynthia Dwork and Vitaly Feldman · 2018
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Property testing for differential privacy
Anna Gilbert and Audra McMillan · 2018
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Preventing fairness gerrymandering: Auditing and learning for subgroup fairness
Michael J. Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu · 2018
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