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We explicitly construct the first nontrivial extractors for degree $d \ge 2$ polynomial sources over $\mathbb{F}_2^n$. Our extractor requires min-entropy $k\geq n - \tilde{\Omega}(\sqrt{\log n})$. Previously, no constructions were known,…

Computational Complexity · Computer Science 2024-02-02 Eshan Chattopadhyay , Jesse Goodman , Mohit Gurumukhani

We propose a new model of a weakly random source that admits randomness extraction. Our model of additive sources includes such natural sources as uniform distributions on arithmetic progressions (APs), generalized arithmetic progressions…

Computational Complexity · Computer Science 2014-10-28 Abhishek Bhowmick , Ariel Gabizon , Thái Hoàng Lê , David Zuckerman

In this paper, two structural results concerning low degree polynomials over finite fields are given. The first states that over any finite field $\mathbb{F}$, for any polynomial $f$ on $n$ variables with degree $d \le \log(n)/10$, there…

Computational Complexity · Computer Science 2014-04-03 Gil Cohen , Avishay Tal

We describe a construction of explicit affine extractors over large finite fields with exponentially small error and linear output length. Our construction relies on a deep theorem of Deligne giving tight estimates for exponential sums over…

Computational Complexity · Computer Science 2014-01-27 Jean Bourgain , Zeev Dvir , Ethan Leeman

We continue a line of work on extracting random bits from weak sources that are generated by simple processes. We focus on the model of locally samplable sources, where each bit in the source depends on a small number of (hidden) uniformly…

Computational Complexity · Computer Science 2022-05-30 Omar Alrabiah , Eshan Chattopadhyay , Jesse Goodman , Xin Li , João Ribeiro

We prove that random low-degree polynomials (over $\mathbb{F}_2$) are unbiased, in an extremely general sense. That is, we show that random low-degree polynomials are good randomness extractors for a wide class of distributions. Prior to…

Computational Complexity · Computer Science 2025-04-22 Omar Alrabiah , Jesse Goodman , Jonathan Mosheiff , João Ribeiro

We study the problem of extracting random bits from weak sources that are sampled by algorithms with limited memory. This model of small-space sources was introduced by Kamp, Rao, Vadhan and Zuckerman (STOC'06), and falls into a line of…

Computational Complexity · Computer Science 2021-08-25 Eshan Chattopadhyay , Jesse Goodman

We consider the problem of extracting randomness from \textit{sumset sources}, a general class of weak sources introduced by Chattopadhyay and Li (STOC, 2016). An $(n,k,C)$-sumset source $\mathbf{X}$ is a distribution on $\{0,1\}^n$ of the…

Computational Complexity · Computer Science 2021-10-26 Eshan Chattopadhyay , Jyun-Jie Liao

We provide a unified method for constructing explicit distributions which are difficult for restricted models of computation to generate. Our constructions are based on a new notion of robust extractors, which are extractors that remain…

Computational Complexity · Computer Science 2026-05-11 Farzan Byramji , Daniel M. Kane , Jackson Morris , Anthony Ostuni

Diffusion generative models have emerged as a new challenger to popular deep neural generative models such as GANs, but have the drawback that they often require a huge number of neural function evaluations (NFEs) during synthesis unless…

Machine Learning · Statistics 2022-10-12 Hideyuki Tachibana , Mocho Go , Muneyoshi Inahara , Yotaro Katayama , Yotaro Watanabe

This work is based on the proposal of a deterministic randomness extractor of a random Diffie-Hellman element defined over two prime order multiplicative subgroups of a finite fields $\mathbb{F}_{p^n}$, $G_1$ and $G_2$. We show that the…

Cryptography and Security · Computer Science 2015-02-03 Hortense Boudjou Tchapgnouo , Abdoul Aziz Ciss

We give simple criteria to identify the exponential order of magnitude of the absolute value of the determinant for wide classes of random matrix models, not requiring the assumption of invariance. These include Gaussian matrices with…

Probability · Mathematics 2023-02-22 Gérard Ben Arous , Paul Bourgade , Benjamin McKenna

Let $\mathcal{F}$ be a finite alphabet and $\mathcal{D}$ be a finite set of distributions over $\mathcal{F}$. A Generalized Santha-Vazirani (GSV) source of type $(\mathcal{F}, \mathcal{D})$, introduced by Beigi, Etesami and Gohari (ICALP…

Computational Complexity · Computer Science 2017-09-12 Salman Beigi , Andrej Bogdanov , Omid Etesami , Siyao Guo

We consider the problem of uniform sampling of points on an algebraic variety. Specifically, we develop a randomized algorithm that, given a small set of multivariate polynomials over a sufficiently large finite field, produces a common…

Data Structures and Algorithms · Computer Science 2009-02-10 Mahdi Cheraghchi , Amin Shokrollahi

We continue the study of constructing explicit extractors for independent general weak random sources. The ultimate goal is to give a construction that matches what is given by the probabilistic method --- an extractor for two independent…

Computational Complexity · Computer Science 2015-03-10 Xin Li

Consider the set $E(D, N)$ of all bivariate exponential polynomials $$ f(\xi, \eta) = \sum_{j=1}^n p_j(\xi, \eta) e^{2\pi i (x_j\xi+y_j\eta)}, $$ where the polynomials $p_j \in \mathbb{C}[\xi, \eta]$ have degree $<D$, $n\le N$ and where…

Classical Analysis and ODEs · Mathematics 2025-10-15 Mihail N. Kolountzakis , Emmanuil Spyridakis

In this work, we present a novel neural network to generate high resolution images. We replace the decoder of VAE with a discriminator while using the encoder as it is. The encoder is fed data from a normal distribution while the generator…

Image and Video Processing · Electrical Eng. & Systems 2021-06-23 Abhinav Sagar

Let $f, f_1, \ldots, f_\nV$ be polynomials with rational coefficients in the indeterminates $\bfX=X_1, \ldots, X_n$ of maximum degree $D$ and $V$ be the set of common complex solutions of $\F=(f_1,\ldots, f_\nV)$. We give an algorithm…

Symbolic Computation · Computer Science 2014-05-08 Aurélien Greuet , Mohab Safey El Din

We study the problem of extracting randomness from somewhere-random sources, and related combinatorial phenomena: partition analogues of Shearer's lemma on projections. A somewhere-random source is a tuple $(X_1, \ldots, X_t)$ of (possibly…

Combinatorics · Mathematics 2023-06-30 Swastik Kopparty , Vishvajeet N

A Santha-Vazirani (SV) source is a sequence of random bits where the conditional distribution of each bit, given the previous bits, can be partially controlled by an adversary. Santha and Vazirani show that deterministic randomness…

Computational Complexity · Computer Science 2014-12-23 Salman Beigi , Omid Etesami , Amin Gohari
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