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We show examples of total Boolean functions that depend on $n$ variables and have spectral sensitivity $\Theta(\sqrt{\log n})$, which is asymptotically minimal. Our main new function combines the Hamming code with the Boolean address…

Computational Complexity · Computer Science 2025-02-21 Krišjānis Prūsis , Jevgēnijs Vihrovs

Given query access to a monotone function $f\colon\{0,1\}^n\to\{0,1\}$ with certificate complexity $C(f)$ and an input $x^{\star}$, we design an algorithm that outputs a size-$C(f)$ subset of $x^{\star}$ certifying the value of…

Data Structures and Algorithms · Computer Science 2022-04-05 Meghal Gupta , Naren Sarayu Manoj

Let $X_1, X_2,\dots$ be a short-memory linear process of random variables. For $1\leq q<2$, let $\cF$ be a bounded set of real-valued functions on $[0,1]$ with finite $q$-variation. It is proved that…

Probability · Mathematics 2019-09-26 Rimas Norvaiša , Alfredas Račkauskas

Suppose $f : [0,1]^2 \rightarrow \mathbb{R}$ is a $(c,\alpha)$-mixed H\"older function that we sample at $l$ points $X_1,\ldots,X_l$ chosen uniformly at random from the unit square. Let the location of these points and the function values…

Classical Analysis and ODEs · Mathematics 2022-03-03 Nicholas F. Marshall

We prove a functional limit theorem in a space of analytic functions for the random Dirichlet series $D(\alpha;z)=\sum_{n\geq 2}(\log n)^{\alpha}(\eta_n+{\rm i} \theta_n)/n^z$, properly scaled and normalized, where…

Probability · Mathematics 2022-11-02 Dariusz Buraczewski , Congzao Dong , Alexander Iksanov , Alexander Marynych

We consider the problem of testing whether an unknown Boolean function $f$ is monotone versus $\epsilon$-far from every monotone function. The two main results of this paper are a new lower bound and a new algorithm for this well-studied…

Computational Complexity · Computer Science 2014-12-19 Xi Chen , Rocco A. Servedio , Li-Yang Tan

We show that unbounded fan-in boolean formulas of depth $d+1$ and size $s$ have average sensitivity $O(\frac{1}{d}\log s)^d$. In particular, this gives a tight $2^{\Omega(d(n^{1/d}-1))}$ lower bound on the size of depth $d+1$ formulas…

Computational Complexity · Computer Science 2015-09-01 Benjamin Rossman

Many applications that use empirically estimated functions face a curse of dimensionality, because the integrals over most function classes must be approximated by sampling. This paper introduces a novel regression-algorithm that learns…

Machine Learning · Computer Science 2015-03-31 Wendelin Böhmer , Klaus Obermayer

Let $\epsilon_{1},\ldots,\epsilon_{n}$ be a sequence of independent Rademacher random variables. We prove that there is a constant $c>0$ such that for any unit vectors $v_1,\ldots,v_n\in \mathbb{R}^2$, $$\Pr\left[||\epsilon_1…

Probability · Mathematics 2024-12-31 Xiaoyu He , Tomas Juskevicius , Bhargav Narayanan , Sam Spiro

Let $f$ be a polynomial $f$ of degree $d\ge 2$ with integer coefficients which is irreducible over the rationals. Cilleruelo conjectured that the least common multiple of the values of the polynomial at the first $N$ integers satisfies…

Number Theory · Mathematics 2020-01-29 James Maynard , Zeev Rudnick

We define tests of boolean functions which distinguish between linear (or quadratic) polynomials, and functions which are very far, in an appropriate sense, from these polynomials. The tests have optimal or nearly optimal trade-offs between…

Combinatorics · Mathematics 2007-05-23 Alex Samorodnitsky

We consider functions on the $d$-dimensional unit cube whose partial derivatives up to order $r$ are bounded by one. It is known that the minimal number of function values that is needed to approximate the integral of such functions up to…

Numerical Analysis · Mathematics 2018-10-09 David Krieg

A sharp, distribution free, non-asymptotic result is proved for the concentration of a random function around the mean function, when the randomization is generated by a finite sequence of independent data and the random functions satisfy…

Probability · Mathematics 2023-12-25 Thomas Anton , Sutanuka Roy , Rabee Tourky

We show that for any constant $c>0$, any (two-sided error) adaptive algorithm for testing monotonicity of Boolean functions must have query complexity $\Omega(n^{1/2-c})$. This improves the $\tilde\Omega(n^{1/3})$ lower bound of [CWX17] and…

Computational Complexity · Computer Science 2025-11-10 Mark Chen , Xi Chen , Hao Cui , William Pires , Jonah Stockwell

We consider the problem of learning multi-ridge functions of the form f(x) = g(Ax) from point evaluations of f. We assume that the function f is defined on an l_2-ball in R^d, g is twice continuously differentiable almost everywhere, and A…

Machine Learning · Statistics 2016-06-07 Hemant Tyagi , Volkan Cevher

We give an improved lower bound for the error of any quadrature computing $\int_{-1}^1 f(x) d\alpha(x)$ of analytic functions bounded in the neighborhood of $[-1,1]$.

Numerical Analysis · Mathematics 2015-03-17 Małgorzata Moczurad , Piotr Zgliczyński , Włodzimierz Zwonek

Let $f:\{-1,1\}^{n}\rightarrow \{-1,1\}$ be a Boolean valued function having total degree $d$. Then a conjecture due to Servedio and Gopalan asserts that $\sum_{i=1}^{n}\widehat{f}(i)\leq \sum_{j=1}^{d}\widehat{\text{Maj}}_{d}(j)$ where…

Computational Complexity · Computer Science 2016-11-08 Sumit Kumar Jha

This work examines the problem of learning an unknown von Neumann measurement of dimension $d$ from a finite number of copies. To obtain a faithful approximation of the given measurement we are allowed to use it $N$ times. Our main goal is…

Quantum Physics · Physics 2022-11-30 Paulina Lewandowska , Ryszard Kukulski , Łukasz Pawela , Zbigniew Puchała

Let $\lambda$ the Barban--Vehov weights, defined in $(1)$. Let $X\ge z_1\ge100$ and $z_2=z_1^\tau$ for some $\tau>1$. We prove that \begin{equation*} \sum_{n\le X}\frac{1}{n}\Bigl(\sum_{\substack{d|n}}\lambda_d\Bigr)^2 \le f(\tau)\frac{\log…

Number Theory · Mathematics 2025-12-15 Olivier Ramaré , Sebastian Zuniga Alterman

The degree-$d$ Chow parameters of a Boolean function $f: \{-1,1\}^n \to \mathbb{R}$ are its degree at most $d$ Fourier coefficients. It is well-known that degree-$d$ Chow parameters uniquely characterize degree-$d$ polynomial threshold…

Machine Learning · Computer Science 2018-11-09 Ilias Diakonikolas , Daniel M. Kane