6 papers
A general framework for computation and estimation using the saddlepoint approximation
Godrick Oketch, Rachel M. Fewster, Jesse Goodman
The saddlepoint approximation provides highly accurate approximations to probability density and mass functions using only the corresponding moment generating functions (MGFs). Rec…
Leakage-Resilient Extractors against Number-on-Forehead Protocols
Eshan Chattopadhyay, Jesse Goodman
Given a sequence of independent sources , how many of them must be good (i.e., contain some min-entropy) in order to…
What is the price of approximation? The saddlepoint approximation to a likelihood function
Godrick Oketch, Rachel M. Fewster, Jesse Goodman
The saddlepoint approximation to the likelihood, and its corresponding maximum likelihood estimate (MLE), offer an alternative estimation method when the true likelihood is intract…
Improved Condensers for Chor-Goldreich Sources
Jesse Goodman, Xin Li, David Zuckerman
One of the earliest models of weak randomness is the Chor-Goldreich (CG) source. A -CG source is a sequence of random variables , where…
Low-Degree Polynomials Are Good Extractors
Omar Alrabiah, Jesse Goodman, Jonathan Mosheiff +1
We prove that random low-degree polynomials (over ) are unbiased, in an extremely general sense. That is, we show that random low-degree polynomials are good randomne…
Extractors for Polynomial Sources over
Eshan Chattopadhyay, Jesse Goodman, Mohit Gurumukhani
We explicitly construct the first nontrivial extractors for degree polynomial sources over . Our extractor requires min-entropy $k\geq n - \tildeΩ(\sqrt{\…