The reusable holdout: Preserving validity in adaptive data analysis
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Toniann Pitassi | Vitaly Feldman | Omer Reingold | Aaron Roth | Cynthia Dwork | Moritz Hardt | C. Dwork | Aaron Roth | V. Feldman | Moritz Hardt | T. Pitassi | O. Reingold | Omer Reingold
[1] D. Freedman. A Note on Screening Regression Equations , 1983 .
[2] W. E. Riggs. An Experimental Evaluation , 1983 .
[3] Y. Benjamini,et al. Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .
[4] M. Kearns. Efficient noise-tolerant learning from statistical queries , 1998, JACM.
[5] André Elisseeff,et al. Stability and Generalization , 2002, J. Mach. Learn. Res..
[6] VACANT-PROPERTY Policy,et al. THE BROOKINGS INSTITUTION , 2002 .
[7] Juha Reunanen,et al. Overfitting in Making Comparisons Between Variable Selection Methods , 2003, J. Mach. Learn. Res..
[8] T. Poggio,et al. General conditions for predictivity in learning theory , 2004, Nature.
[9] D. Ruppert. The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .
[10] J. Ioannidis. Why Most Published Research Findings Are False , 2005, PLoS medicine.
[11] Moni Naor,et al. Our Data, Ourselves: Privacy Via Distributed Noise Generation , 2006, EUROCRYPT.
[12] Kunle Olukotun,et al. Map-Reduce for Machine Learning on Multicore , 2006, NIPS.
[13] Cynthia Dwork,et al. Calibrating Noise to Sensitivity in Private Data Analysis , 2006, TCC.
[14] Mohammad Taghi Hajiaghayi,et al. Regret minimization and the price of total anarchy , 2008, STOC.
[15] C. Apté,et al. Proceedings of the SIAM International Conference on Data Mining, SDM 2008, April 24-26, 2008, Atlanta, Georgia, USA , 2010, SDM.
[16] Dean P. Foster,et al. α‐investing: a procedure for sequential control of expected false discoveries , 2008 .
[17] R. Rosenfeld. Nature , 2009, Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery.
[18] Adam Tauman Kalai,et al. On the equilibria of alternating move games , 2010, SODA '10.
[19] Ohad Shamir,et al. Learnability, Stability and Uniform Convergence , 2010, J. Mach. Learn. Res..
[20] Tim Roughgarden,et al. Interactive privacy via the median mechanism , 2009, STOC '10.
[21] Gavin C. Cawley,et al. On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation , 2010, J. Mach. Learn. Res..
[22] Leif D. Nelson,et al. False-Positive Psychology , 2011, Psychological science.
[23] Aaron Roth,et al. Privately releasing conjunctions and the statistical query barrier , 2010, STOC '11.
[24] Saharon Rosset,et al. The Quality Preserving Database: A Computational Framework for Encouraging Collaboration, Enhancing Power and Controlling False Discovery , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[25] Aaron Roth,et al. Fast Private Data Release Algorithms for Sparse Queries , 2013, APPROX-RANDOM.
[26] Aaron Roth,et al. A learning theory approach to non-interactive database privacy , 2008, STOC.
[27] Nicole Immorlica,et al. Constrained signaling for welfare and revenue maximization , 2013, SECO.
[28] Zhiyi Huang,et al. New techniques for computation over private data , 2013 .
[29] Paul W. Goldberg,et al. Bounds for the Query Complexity of Approximate Equilibria , 2016, ACM Trans. Economics and Comput..
[30] Justin Hsu,et al. Differential privacy for the analyst via private equilibrium computation , 2012, STOC '13.
[31] Aaron Roth,et al. The Algorithmic Foundations of Differential Privacy , 2014, Found. Trends Theor. Comput. Sci..
[32] Aaron Roth,et al. Exploiting Metric Structure for Efficient Private Query Release , 2014, SODA.
[33] Marco Gaboardi,et al. Dual Query: Practical Private Query Release for High Dimensional Data , 2014, ICML.
[34] Gilles Barthe,et al. Higher-Order Approximate Relational Refinement Types for Mechanism Design and Differential Privacy , 2014, POPL.
[35] Andreas Haeberlen,et al. Differential Privacy: An Economic Method for Choosing Epsilon , 2014, 2014 IEEE 27th Computer Security Foundations Symposium.
[36] A. Gelman,et al. The statistical crisis in science , 2014 .
[37] Aaron Roth,et al. Inducing Approximately Optimal Flow Using Truthful Mediators , 2015, EC.
[38] Aaron Roth,et al. Accuracy for Sale: Aggregating Data with a Variance Constraint , 2015, ITCS.
[39] Adel Javanmard,et al. On Online Control of False Discovery Rate , 2015, ArXiv.
[40] Aaron Roth,et al. Watch and learn: optimizing from revealed preferences feedback , 2015, SECO.
[41] Sampath Kannan,et al. Approximately Stable, School Optimal, and Student-Truthful Many-to-One Matchings (via Differential Privacy) , 2014, SODA.
[42] Salil P. Vadhan,et al. Theory of Cryptography , 2016, Lecture Notes in Computer Science.
[43] Aaron Roth,et al. Adaptive Learning with Robust Generalization Guarantees , 2016, COLT.
[44] Aaron Roth,et al. Fairness in Learning: Classic and Contextual Bandits , 2016, NIPS.
[45] Aaron Roth,et al. Learning from Rational Behavior: Predicting Solutions to Unknown Linear Programs , 2016, NIPS.
[46] Aaron Roth,et al. The Strange Case of Privacy in Equilibrium Models , 2015, EC.