M
Michael Mitzenmacher
Researcher at Harvard University
Publications - 434
Citations - 39329
Michael Mitzenmacher is an academic researcher from Harvard University. The author has contributed to research in topics: Hash function & Cuckoo hashing. The author has an hindex of 79, co-authored 422 publications receiving 36300 citations. Previous affiliations of Michael Mitzenmacher include University of Paris-Sud & International Computer Science Institute.
Papers
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Book
Probability and Computing: Randomized Algorithms and Probabilistic Analysis
Michael Mitzenmacher,Eli Upfal +1 more
TL;DR: Preface 1. Events and probability 2. Discrete random variables and expectation 3. Moments and deviations 4. Chernoff bounds 5. Balls, bins and random graphs 6. Probabilistic method 7. Markov chains and random walks 8. Continuous distributions and the Poisson process
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Detecting Novel Associations in Large Data Sets
David N. Reshef,David N. Reshef,David N. Reshef,Yakir A. Reshef,Yakir A. Reshef,Hilary K. Finucane,Sharon R. Grossman,Sharon R. Grossman,Gilean McVean,Gilean McVean,Peter J. Turnbaugh,Eric S. Lander,Eric S. Lander,Eric S. Lander,Michael Mitzenmacher,Pardis C. Sabeti,Pardis C. Sabeti +16 more
TL;DR: A measure of dependence for two-variable relationships: the maximal information coefficient (MIC), which captures a wide range of associations both functional and not, and for functional relationships provides a score that roughly equals the coefficient of determination of the data relative to the regression function.
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Network Applications of Bloom Filters: A Survey
TL;DR: The aim of this paper is to survey the ways in which Bloom filters have been used and modified in a variety of network problems, with the aim of providing a unified mathematical and practical framework for understanding them and stimulating their use in future applications.
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A Brief History of Generative Models for Power Law and Lognormal Distributions
TL;DR: A rich and long history is found of how lognormal distributions have arisen as a possible alternative to power law distributions across many fields, focusing on underlying generative models that lead to these distributions.
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The power of two choices in randomized load balancing
TL;DR: This work uses a limiting, deterministic model representing the behavior as n/spl rarr//spl infin/ to approximate the behavior of finite systems and provides simulations that demonstrate that the method accurately predicts system behavior, even for relatively small systems.