J
John Peebles
Researcher at Massachusetts Institute of Technology
Publications - 13
Citations - 495
John Peebles is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Sublinear function & Probability distribution. The author has an hindex of 6, co-authored 11 publications receiving 407 citations. Previous affiliations of John Peebles include Harvey Mudd College & Princeton University.
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An Extreme Case of Plant–Insect Codiversification: Figs and Fig-Pollinating Wasps
Astrid Cruaud,Nina Rønsted,Nina Rønsted,Nina Rønsted,Bhanumas Chantarasuwan,Lien-Siang Chou,Wendy L. Clement,Wendy L. Clement,Arnaud Couloux,Benjamin R. Cousins,Gwenaëlle Genson,Rhett D. Harrison,Paul C. Hanson,Martine Hossaert-McKey,Roula Jabbour-Zahab,Emmanuelle Jousselin,Carole Kerdelhué,Finn Kjellberg,Carlos Lopez-Vaamonde,John Peebles,Yan-Qiong Peng,Rodrigo Augusto Santinelo Pereira,Tselil Schramm,Rosichon Ubaidillah,Simon Van Noort,George D. Weiblen,Da-Rong Yang,Anak Yodpinyanee,Ran Libeskind-Hadas,James M. Cook,Jean-Yves Rasplus,Vincent Savolainen,Vincent Savolainen +32 more
TL;DR: Biogeographic analyses indicate that the present-day distribution of fig and pollinator lineages is consistent with a Eurasian origin and subsequent dispersal, rather than with Gondwanan vicariance.
Collision-based Testers are Optimal for Uniformity and Closeness
TL;DR: In this paper, the authors show that collision-based testers are information-theoretically optimal, up to constant factors, both in the dependence on the domain size and in the dependency on the error parameter.
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Sample-Optimal Identity Testing with High Probability
TL;DR: In this article, the authors studied the problem of testing identity against a given distribution with a focus on the high confidence regime, and showed that the optimal sample complexity of identity testing is O(1) for any ε = o(1), where ε < 1.
Posted Content
Collision-based Testers are Optimal for Uniformity and Closeness
TL;DR: In this article, the authors show that collision-based testers are information-theoretically optimal, up to constant factors, both in the dependence on the domain size and in the dependency on the error parameter.
Journal Article
Sublinear-Time Algorithms for Counting Star Subgraphs via Edge Sampling
Maryam Aliakbarpour,Amartya Shankha Biswas,Themistoklis Gouleakis,John Peebles,Anak Yodpinyanee,Ronitt Rubinfeld +5 more
TL;DR: In this paper, the authors used the National Science Foundation (US) Graduate Research Fellowship Program (GCF) grant CCF-1217423 to conduct an experimental study on the effect of the presence of DNA mutations.