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When the target of statistical inference is chosen in a data-driven manner, the guarantees provided by classical theories vanish.
An algorithm for quadratic programming
Marguerite Frank and Philip Wolfe · 1956
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The file drawer problem and tolerance for null results
Robert Rosenthal · 1979
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Regression shrinkage and selection via the lasso
Robert Tibshirani · 1996
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The Analysis of Variance
Henry Scheffe · 1999
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Stability and generalization
Olivier Bousquet and André Elisseeff · 2002
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An introduction to variable and feature selection
Isabelle Guyon and André Elisseeff · 2003
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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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A (tight) upper bound for the length of confidence intervals with conditional coverage
Danijel Kivaranovic and Hannes Leeb · 2007
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Berry-Esseen bounds for projection parameters and partial correlations with increasing dimension
Arun Kumar Kuchibhotla, Alessandro Rinaldo, and Larry Wasserman · 2007
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Sure independence screening for ultrahigh dimensional feature space
Jianqing Fan and Jinchi Lv · 2008
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Bounds on the sample complexity for private learning and private data release
Amos Beimel, Shiva Prasad Kasiviswanathan, and Kobbi Nissim · 2010
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Simultaneous and selective inference: Current successes and future challenges
Yoav Benjamini · 2010
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Coresets, sparse greedy approximation, and the Frank-Wolfe algorithm
Kenneth L Clarkson · 2010
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Boosting and differential privacy
Cynthia Dwork, Guy N Rothblum, and Salil Vadhan · 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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Valid post-selection inference
Richard Berk, Lawrence Brown, Andreas Buja, Kai Zhang, and Linda Zhao · 2013
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Revisiting Frank-Wolfe: Projection-free sparse convex optimization
Martin Jaggi · 2013
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Differentially private feature selection via stability arguments, and the robustness of the lasso
Abhradeep Guha Thakurta and Adam Smith · 2013
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The Algorithmic Foundations of Differential Privacy , volume 9
Cynthia Dwork and Aaron Roth · 2014
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Optimal inference after model selection
William Fithian, Dennis Sun, and Jonathan Taylor · 2014
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Exact post model selection inference for marginal screening
Jason D Lee and Jonathan E Taylor · 2014
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Nearly optimal private LASSO
Kunal Talwar, Abhradeep Guha Thakurta, and Li Zhang · 2015
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Statistical learning and selective inference
Jonathan Taylor and Robert J Tibshirani · 2015
An MCMC-free approach to post-selective inference
Snigdha Panigrahi, Jelena Markovic, and Jonathan Taylor · 2017
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Tight lower bounds for differentially private selection
Thomas Steinke and Jonathan Ullman · 2017
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Differentially private model selection with penalized and constrained likelihood
Jing Lei, Anne Sophie Charest, Aleksandra Slavkovic, Adam Smith, and Stephen Fienberg · 2018
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More powerful post-selection inference, with application to the Lasso
Keli Liu, Jelena Markovic, and Robert Tibshirani · 2018
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Conditional predictive inference for high-dimensional stable algorithms
Lukas Steinberger and Hannes Leeb · 2018
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Raef Bassily and Yoav Freund · 2016
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Algorithmic stability for adaptive data analysis
Raef Bassily, Kobbi Nissim, Adam Smith, Thomas Steinke, Uri Stemmer, and Jonathan Ullman · 2016
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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
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Exact post-selection inference, with application to the lasso
Jason D Lee, Dennis L Sun, Yuekai Sun, and Jonathan E Taylor · 2016
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Bootstrap inference after using multiple queries for model selection
Jelena Markovic and Jonathan Taylor · 2016
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Selective inference with a randomized response
Xiaoying Tian and Jonathan Taylor · 2018
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Inference on winners
Isaiah Andrews, Toru Kitagawa, and Adam McCloskey · 2019
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Confidence intervals for selected parameters
Yoav Benjamini, Yotam Hechtlinger, and Philip B Stark · 2019
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Models as approximations I: Consequences illustrated with linear regression
Andreas Buja, Lawrence Brown, Richard Berk, Edward George, Emil Pitkin, Mikhail Traskin, Kai Zhang, and Linda Zhao · 2019
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Practical differentially private top-k selection with pay-what-you-get composition
David Durfee and Ryan M Rogers · 2019
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Arun K Kuchibhotla, Lawrence D Brown, Andreas Buja, and Junhui Cai · 2019
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Approximate selective inference via maximum likelihood
Snigdha Panigrahi and Jonathan Taylor · 2019
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Bootstrapping and sample splitting for high-dimensional, assumption-lean inference
Alessandro Rinaldo, Larry Wasserman, and Max G’Sell · 2019
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Uniformly valid confidence intervals post-model-selection
François Bachoc, David Preinerstorfer, and Lukas Steinberger · 2020
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Inferactive data analysis
Nan Bi, Jelena Markovic, Lucy Xia, and Jonathan Taylor · 2020
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Predictive inference with the jackknife+
Rina Foygel Barber, Emmanuel J Candes, Aaditya Ramdas, and Ryan J Tibshirani · 2021
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Splitting strategies for post-selection inference
Daniel G Rasines and G Alastair Young · 2021
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