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We give a new proof of the "transfer theorem" underlying adaptive data analysis: that any mechanism for answering adaptively chosen statistical queries that is differentially private and sample-accurate is also accurate out-of-sample.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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The statistical crisis in science
Andrew Gelman and Eric Loken · 2014
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Preventing false discovery in interactive data analysis is hard
Moritz Hardt and Jonathan Ullman · 2014
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Interactive fingerprinting codes and the hardness of preventing false discovery
Thomas Steinke and Jonathan Ullman · 2015
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Algorithmic stability for adaptive data analysis
Raef Bassily, Kobbi Nissim, Adam Smith, Thomas Steinke, Uri Stemmer, and Jonathan Ullman · 2016
Earlier work this paper cites.
Concentrated differential privacy: Simplifications, extensions, and lower bounds
Mark Bun and Thomas Steinke · 2016
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Adaptive learning with robust generalization guarantees
Rachel Cummings, Katrina Ligett, Kobbi Nissim, Aaron Roth, and Zhiwei Steven Wu · 2016
Earlier work this paper cites.
Max-information, differential privacy, and post-selection hypothesis testing
Ryan Rogers, Aaron Roth, Adam Smith, and Om Thakkar · 2016
Cited alongside, same era.
Controlling bias in adaptive data analysis using information theory
Daniel Russo and James Zou · 2016
Cited alongside, same era.
Generalization for adaptively-chosen estimators via stable median
Vitaly Feldman and Thomas Steinke · 2017
Cited alongside, same era.
Subgaussian tail bounds via stability arguments
Thomas Steinke and Jonathan Ullman · 2017
Cited alongside, same era.
Information-theoretic analysis of generalization capability of learning algorithms
Aolin Xu and Maxim Raginsky · 2017
Cited alongside, same era.
Calibrating noise to variance in adaptive data analysis
Mitigating bias in adaptive data gathering via differential privacy
Seth Neel and Aaron Roth · 2018
Later among the works it cites.
Why adaptively collected data have negative bias and how to correct for it
Xinkun Nie, Xiaoying Tian, Jonathan Taylor, and James Zou · 2018
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The advantages of multiple classes for reducing overfitting from test set reuse
Vitaly Feldman, Roy Frostig, and Moritz Hardt · 2019
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A necessary and sufficient stability notion for adaptive generalization
Katrina Ligett and Moshe Shenfeld · 2019
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Concentration bounds for high sensitivity functions through differential privacy
Kobbi Nissim and Uri Stemmer · 2019
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Guaranteed validity for empirical approaches to adaptive data analysis
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Vitaly Feldman and Thomas Steinke · 2018
Cited alongside, same era.
Generalization bounds for uniformly stable algorithms
Vitaly Feldman and Jan Vondrak · 2018
Cited alongside, same era.
Generalization in adaptive data analysis and holdout reuse
Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toni Pitassi, Omer Reingold, and Aaron Roth
Cited in the paper.
The reusable holdout: Preserving validity in adaptive data analysis
Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Aaron Roth
Cited in the paper.
Preserving statistical validity in adaptive data analysis
Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Aaron Leon Roth
Cited in the paper.
Challenges in bayesian adaptive data analysis
Sam Elder
Cited in the paper.
Bayesian adaptive data analysis guarantees from subgaussianity
Sam Elder
Cited in the paper.
Ryan Rogers, Aaron Roth, Adam Smith, Nathan Srebro, Om Thakkar, and Blake Woodworth · 2019
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Natural analysts in adaptive data analysis
Tijana Zrnic and Moritz Hardt · 2019
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