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Assume that for every derandomization result for logspace algorithms, there is a pseudorandom generator strong enough to nearly recover the derandomization by iterating over all seeds and taking a majority vote. We prove under a precise…

Computational Complexity · Computer Science 2017-04-11 William M. Hoza , Chris Umans

We devise a new pseudorandom generator against degree 2 polynomial threshold functions in the Gaussian setting. We manage to achieve $\epsilon$ error with seed length polylogarithmic in $\epsilon$ and the dimension, and exponential…

Computational Complexity · Computer Science 2014-04-07 Daniel M. Kane

We obtain new explicit pseudorandom generators for several computational models involving groups. Our main results are as follows: 1. We consider read-once group-products over a finite group $G$, i.e., tests of the form $\prod_{i=1}^n…

Computational Complexity · Computer Science 2025-06-05 Chin Ho Lee , Emanuele Viola

A central question in derandomization is whether randomized logspace (RL) equals deterministic logspace (L). To show that RL=L, it suffices to construct explicit pseudorandom generators (PRGs) that fool polynomial-size read-once (oblivious)…

Computational Complexity · Computer Science 2018-08-21 Michael A. Forbes , Zander Kelley

Let x be a random vector coming from any k-wise independent distribution over {-1,1}^n. For an n-variate degree-2 polynomial p, we prove that E[sgn(p(x))] is determined up to an additive epsilon for k = poly(1/epsilon). This answers an open…

Computational Complexity · Computer Science 2010-02-18 Ilias Diakonikolas , Daniel M. Kane , Jelani Nelson

We present an explicit pseudorandom generator for oblivious, read-once, permutation branching programs of constant width that can read their input bits in any order. The seed length is $O(\log^2 n)$, where $n$ is the length of the branching…

Computational Complexity · Computer Science 2013-06-21 Omer Reingold , Thomas Steinke , Salil Vadhan

A sliding-window algorithm of window size $t$ is an algorithm whose current operation depends solely on the last $t$ symbols read. We construct pseudorandom generators (PRGs) for low-space randomized sliding-window algorithms that have…

Computational Complexity · Computer Science 2023-01-19 Augusto Modanese

We show a new PRG construction fooling depth-$d$, size-$m$ $\mathsf{AC}^0$ circuits within error $\varepsilon$, which has seed length $O(\log^{d-1}(m)\log(m/\varepsilon)\log\log(m))$. Our PRG improves on previous work (Trevisan and Xue…

Computational Complexity · Computer Science 2023-01-25 Xin Lyu

We construct a pseudorandom generator which fools read-$k$ oblivious branching programs and, more generally, any linear length oblivious branching program, assuming that the sequence according to which the bits are read is known in advance.…

Computational Complexity · Computer Science 2017-08-08 Rohit Gurjar , Ben Lee Volk

We study the problem of computationally efficient proper agnostic learning of multidimensional concept classes under the Gaussian distribution. In this setting, given i.i.d. labeled samples from an unknown distribution over $\mathbb{R}^d…

Data Structures and Algorithms · Computer Science 2026-05-28 Sergei Tikhonov , Arsen Vasilyan

The paper study counter-dependent pseudorandom generators; the latter are generators such that their state transition function (and output function) is being modified dynamically while working: For such a generator the recurrence sequence…

Cryptography and Security · Computer Science 2011-11-15 Vladimir Anashin

We use the generating function approach to derive simple expressions for the factorial moments of the distance distribution over Reed-Solomon codes. We obtain better upper bounds for the error term of a counting formula given by Li and Wan,…

Information Theory · Computer Science 2022-05-06 Zhicheng Gao , Jiyou Li

Rubinfeld & Vasilyan recently introduced the framework of testable learning as an extension of the classical agnostic model. It relaxes distributional assumptions which are difficult to verify by conditions that can be checked efficiently…

Machine Learning · Computer Science 2024-11-07 Lucas Slot , Stefan Tiegel , Manuel Wiedmer

Iteration of the modular l-th power function f(x) = x^l (mod n) provides a common pseudorandom number generator (known as the Blum-Blum-Shub generator when l=2). The period of this pseudorandom number generator is closely related to…

Number Theory · Mathematics 2015-06-26 Greg Martin , Carl Pomerance

The behavior of a family of dissipative dynamical systems representing transformations of two-dimensional torus is studied on a discrete lattice and compared with that of conservative hyperbolic automorphisms of the torus. Applying…

Computational Physics · Physics 2011-06-21 L. Yu. Barash

We prove hardness-of-learning results under a well-studied assumption on the existence of local pseudorandom generators. As we show, this assumption allows us to surpass the current state of the art, and prove hardness of various basic…

Machine Learning · Computer Science 2021-06-09 Amit Daniely , Gal Vardi

We study almost symmetric semigroups generated by odd integers. If the embedding dimension is four, we characterize when a symmetric semigroup that is not complete intersection or a pseudo-symmetric semigroup is generated by odd integers.…

Commutative Algebra · Mathematics 2019-01-04 Francesco Strazzanti , Kei-ichi Watanabe

We show that a black-box construction of a pseudorandom generator from a one-way function needs to make Omega(n/log(n)) calls to the underlying one-way function. The bound even holds if the one-way function is guaranteed to be regular. In…

Cryptography and Security · Computer Science 2012-05-22 Thomas Holenstein , Makrand Sinha

We study weighted pseudorandom generators (WPRGs) and derandomizations for read-once branching programs (ROBPs). Denote $n$ and $w$ as the length and the width of a ROBP. We have the following results. For standard ROBPs, we give an…

Computational Complexity · Computer Science 2025-07-22 Kuan Cheng , Ruiyang Wu

We prove new results on the polarizing random walk framework introduced in recent works of Chattopadhyay {et al.} [CHHL19,CHLT19] that exploit $L_1$ Fourier tail bounds for classes of Boolean functions to construct pseudorandom generators…

Computational Complexity · Computer Science 2020-11-10 Eshan Chattopadhyay , Jason Gaitonde , Chin Ho Lee , Shachar Lovett , Abhishek Shetty