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We show an essentially tight bound on the number of adaptively chosen statistical queries that a computationally efficient algorithm can answer accurately given $n$ samples from an unknown distribution.
Teoria statistica delle classi e calcolo delle probabilita
Carlo Emilio Bonferroni · 1936
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Multiple comparisons among means
Olive Jean Dunn · 1961
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On some classical results in probability theory
N. Etemadi · 1985
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Efficient noise-tolerant learning from statistical queries
Michael J. Kearns · 1993
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Jonathan Ullman · 2013
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Mark Bun, Jonathan Ullman, and Salil P. Vadhan · 2014
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Preventing false discovery in interactive data analysis is hard
Moritz Hardt and Jonathan Ullman · 2014
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Jonathan Ullman · 2014
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Preserving statistical validity in adaptive data analysis
Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Aaron Roth · 2015
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Dynamic tardos traitor tracing schemes
T. Laarhoven, J. Doumen, P. Roelse, B. Skoric, and B. de Weger · 2013
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