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Related papers: Fooling Gaussian PTFs via Local Hyperconcentration

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We establish new correlation bounds and pseudorandom generators for a collection of computation models. These models are all natural generalizations of structured low-degree $F_2$-polynomials that we did not have correlation bounds for…

Computational Complexity · Computer Science 2025-01-07 Vinayak M. Kumar

We give the first non-trivial upper bounds on the average sensitivity and noise sensitivity of degree-$d$ polynomial threshold functions (PTFs). These bounds hold both for PTFs over the Boolean hypercube and for PTFs over $\R^n$ under the…

Computational Complexity · Computer Science 2009-10-19 Ilias Diakonikolas , Prasad Raghavendra , Rocco A. Servedio , Li-Yang Tan

A classic result by Carbery and Wright states that a polynomial of Gaussian random variables exhibits anti-concentration in the following sense: for any degree $d$ polynomial $f$, one has the estimate $P( |f(x)| \leq \varepsilon \cdot…

Probability · Mathematics 2023-01-18 Stephen Tu , Ross Boczar

We design new polynomials for representing threshold functions in three different regimes: probabilistic polynomials of low degree, which need far less randomness than previous constructions, polynomial threshold functions (PTFs) with…

Data Structures and Algorithms · Computer Science 2016-08-16 Josh Alman , Timothy M. Chan , Ryan Williams

We show that any distribution on {-1,1}^n that is k-wise independent fools any halfspace h with error \eps for k = O(\log^2(1/\eps) /\eps^2). Up to logarithmic factors, our result matches a lower bound by Benjamini, Gurel-Gurevich, and…

Computational Complexity · Computer Science 2009-02-24 Ilias Diakonikolas , Parikshit Gopalan , Ragesh Jaiswal , Rocco Servedio , Emanuele Viola

We establish generalized Gaussian bounds and local limit theorems with Gaussian-type error for the convolution powers of certain complex-valued functions on $\mathbb{Z}^d$. These global space-times estimates/error, which are sharp in…

Classical Analysis and ODEs · Mathematics 2026-02-17 Pedro H. Alves , Evan Randles

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

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 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

We give improved pseudorandom generators (PRGs) for Lipschitz functions of low-degree polynomials over the hypercube. These are functions of the form psi(P(x)), where P is a low-degree polynomial and psi is a function with small Lipschitz…

Computational Complexity · Computer Science 2012-11-07 Daniel Kane , Raghu Meka

We study the Fourier spectrum of functions $f\colon \{0,1\}^{mk} \to \{-1,0,1\}$ which can be written as a product of $k$ Boolean functions $f_i$ on disjoint $m$-bit inputs. We prove that for every positive integer $d$, \[ \sum_{S \subseteq…

Computational Complexity · Computer Science 2019-02-08 Chin Ho Lee

A degree-$d$ polynomial $p$ in $n$ variables over a field $\F$ is {\em equidistributed} if it takes on each of its $|\F|$ values close to equally often, and {\em biased} otherwise. We say that $p$ has a {\em low rank} if it can be expressed…

Combinatorics · Mathematics 2008-07-02 Tali Kaufman , Shachar Lovett

We present an explicit pseudorandom generator for oblivious, read-once, width-$3$ branching programs, which can read their input bits in any order. The generator has seed length $\tilde{O}( \log^3 n ).$ The previously best known seed length…

Computational Complexity · Computer Science 2014-05-28 Thomas Steinke , Salil Vadhan , Andrew Wan

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

We give the best known pseudorandom generators for two touchstone classes in unconditional derandomization: an $\varepsilon$-PRG for the class of size-$M$ depth-$d$ $\mathsf{AC}^0$ circuits with seed length $\log(M)^{d+O(1)}\cdot…

Computational Complexity · Computer Science 2018-01-12 Rocco A. Servedio , Li-Yang Tan

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 provide asymptotically sharp bounds for the Gaussian surface area and the Gaussian noise sensitivity of polynomial threshold functions. In particular we show that if $f$ is a degree-$d$ polynomial threshold function, then its Gaussian…

Computational Complexity · Computer Science 2009-12-15 Daniel M. Kane

We give a "regularity lemma" for degree-d polynomial threshold functions (PTFs) over the Boolean cube {-1,1}^n. This result shows that every degree-d PTF can be decomposed into a constant number of subfunctions such that almost all of the…

Computational Complexity · Computer Science 2015-03-13 Ilias Diakonikolas , Rocco A. Servedio , Li-Yang Tan , Andrew Wan

We consider the problem of learning high dimensional polynomial transformations of Gaussians. Given samples of the form $p(x)$, where $x\sim N(0, \mathrm{Id}_r)$ is hidden and $p: \mathbb{R}^r \to \mathbb{R}^d$ is a function where every…

Machine Learning · Computer Science 2022-04-11 Sitan Chen , Jerry Li , Yuanzhi Li , Anru R. Zhang

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