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A great deal of effort has been devoted to reducing the risk of spurious scientific discoveries, from the use of sophisticated validation techniques, to deep statistical methods for controlling the false discovery rate in multiple hypothesis testing.
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Controlling the false discovery rate – a practical and powerful approach to multiple testing
Yoav Benjamini and Yosef Hochberg · 1995
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Efficient noise-tolerant learning from statistical queries
Michael Kearns · 1998
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Stability and generalization
Olivier Bousquet and André Elisseeff · 2002
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Revealing information while preserving privacy
Irit Dinur and Kobbi Nissim · 2003
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Overfitting in making comparisons between variable selection methods
Juha Reunanen · 2003
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Privacy-preserving datamining on vertically partitioned databases
Cynthia Dwork and Kobbi Nissim · 2004
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General conditions for predictivity in learning theory
Tomaso Poggio, Ryan Rifkin, Sayan Mukherjee, and Partha Niyogi · 2004
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Practical privacy: the SuLQ framework
Avrim Blum, Cynthia Dwork, Frank McSherry, and Kobbi Nissim · 2005
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Contradicted and initially stronger effects in highly cited clinical research
John A. Ioannidis · 2005
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Why Most Published Research Findings Are False
John P. A. Ioannidis · 2005
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Map-reduce for machine learning on multicore
C. Chu, S. Kim, Y. Lin, Y. Yu, G. Bradski, A. Ng, and K. Olukotun · 2006
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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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Learning theory: stability is sufficient for generalization and necessary and sufficient for consistency of empirical risk minimization
Sayan Mukherjee, Partha Niyogi, Tomaso Poggio, and Ryan Rifkin · 2006
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Alpha-investing: A procedure for sequential control of expected false discoveries
D. Foster and R. Stine · 2008
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On the dangers of cross-validation. an experimental evaluation
R. Bharat Rao and Glenn Fung · 2008
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Trevor Hastie, Robert Tibshirani, and Jerome H. Friedman · 2009
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A multiplicative weights mechanism for privacy-preserving data analysis
Moritz Hardt and Guy N. Rothblum · 2010
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Interactive privacy via the median mechanism
Aaron Roth and Tim Roughgarden · 2010
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Learnability, stability and uniform convergence
Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, and Karthik Sridharan · 2010
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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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http://www.rouli.net/2013/02/five-lessons-from-kaggles-event.html
Five lessons from Kaggle’s event recommendation engine challenge · 2014
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http://blog.kaggle.com/
Kaggle blog: No free hunch · 2014
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https://www.kaggle.com/forums
Kaggle user forums · 2014
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Understanding Machine Learning: From Theory to Algorithms
Shai Shalev-Shwartz and Shai Ben-David · 2014
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Interactive fingerprinting codes and the hardness of preventing false discovery
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The quality preserving database: A computational framework for encouraging collaboration, enhancing power and controlling false discovery
Ehud Aharoni, Hani Neuvirth, and Saharon Rosset · 2011
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A firm foundation for private data analysis
Cynthia Dwork · 2011
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Believe it or not: how much can we rely on published data on potential drug targets?
Florian Prinz, Thomas Schlange, and Khusru Asadullah · 2011
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False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant
Joseph P. Simmons, Leif D. Nelson, and Uri Simonsohn · 2011
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Drug development: Raise standards for preclinical cancer research
C. Glenn Begley and Lee Ellis · 2012
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Statistical algorithms and a lower bound for planted clique
Vitaly Feldman, Elena Grigorescu, Lev Reyzin, Santosh Vempala, and Ying Xiao · 2013
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Thomas Steinke and Jonathan Ullman · 2014
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Learning from the best
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The ladder: A reliable leaderboard for machine learning competitions
Avrim Blum and Moritz Hardt · 2015
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Algorithmic stability for adaptive data analysis
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Generalization in adaptive data analysis and holdout reuse
Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Aaron Roth · 2015
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The reusable holdout: Preserving validity in adaptive data analysis
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Controlling bias in adaptive data analysis using information theory
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Statistical learning and selective inference
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