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We study the problem of approximating and learning coverage functions. A function $c: 2^{[n]} \rightarrow \mathbf{R}^{+}$ is a coverage function, if there exists a universe $U$ with non-negative weights $w(u)$ for each $u \in U$ and subsets…

Machine Learning · Computer Science 2014-05-29 Vitaly Feldman , Pravesh Kothari

We estimate the expected value of certain function $f:\{-1,1\}^{n}\to\mathbb{R}$. For example, with computer assistance, we show that if $\Delta$ is the Laplacian of the Cayley graph of…

Probability · Mathematics 2019-10-15 March T. Boedihardjo

We investigate the generalization of the mistake-bound model to continuous real-valued single variable functions. Let $\mathcal{F}_q$ be the class of absolutely continuous functions $f: [0, 1] \rightarrow \mathbb{R}$ with $||f'||_q \le 1$,…

Machine Learning · Computer Science 2021-06-01 Jesse Geneson

We study the sequential general online regression, known also as the sequential probability assignments, under logarithmic loss when compared against a broad class of experts. We focus on obtaining tight, often matching, lower and upper…

Machine Learning · Computer Science 2023-02-02 Changlong Wu , Mohsen Heidari , Ananth Grama , Wojciech Szpankowski

In this article, we study the logarithm of the central value $L\left(\frac{1}{2}, \chi_D\right)$ in the symplectic family of Dirichlet $L$-functions associated with the hyperelliptic curve of genus $\delta$ over a fixed finite field…

Number Theory · Mathematics 2021-05-25 Pranendu Darbar , Allysa Lumley

In this paper we give a simple proof of the endpoint Besov-Lorentz estimate $$ \|I_\alpha F\|_{\dot{B}^{0,1}_{d/(d-\alpha),1}(\mathbb{R}^d;\mathbb{R}^k)} \leq C \|F \|_{L^1(\mathbb{R}^d;\mathbb{R}^k)} $$ for all $F \in…

Analysis of PDEs · Mathematics 2022-05-27 Felipe Hernandez , Bogdan Raita , Daniel Spector

The logarithm of the number of binary n-variable bent functions is asymptotically less than $11(2^n)/32$ as n tends to infinity. Keywords: boolean function, Walsh--Hadamard transform, plateaued function, bent function, upper bound

Information Theory · Computer Science 2024-11-19 Vladimir N. Potapov

Let $\mathcal{F}_{n}^*$ be the set of Boolean functions depending on all $n$ variables. We prove that for any $f\in \mathcal{F}_{n}^*$, $f|_{x_i=0}$ or $f|_{x_i=1}$ depends on the remaining $n-1$ variables, for some variable $x_i$. This…

Computational Complexity · Computer Science 2015-02-05 Chia-Jung Lee , Satya V. Lokam , Shi-Chun Tsai , Ming-Chuan Yang

Let $\mathsf{TH}_k$ denote the $k$-out-of-$n$ threshold function: given $n$ input Boolean variables, the output is $1$ if and only if at least $k$ of the inputs are $1$. We consider the problem of computing the $\mathsf{TH}_k$ function…

Data Structures and Algorithms · Computer Science 2024-12-24 Ziao Wang , Nadim Ghaddar , Banghua Zhu , Lele Wang

We show that if $f$ is the random completely multiplicative function, the probability that $\sum_{n\le x}\frac{f(n)}{n}$ is positive for every $x$ is at least $1-10^{-45}$, while also strictly smaller than $1$. For large $x$, we prove an…

Number Theory · Mathematics 2022-12-06 Rodrigo Angelo , Max Wenqiang Xu

We propose an adaptive zeroth-order method for minimizing differentiable functions with $L$-Lipschitz continuous gradients. The method is designed to take advantage of the eventual compressibility of the gradient of the objective function,…

Optimization and Control · Mathematics 2025-07-16 Geovani Nunes Grapiglia , Daniel McKenzie

We consider the problem of learning an unknown product distribution $X$ over $\{0,1\}^n$ using samples $f(X)$ where $f$ is a \emph{known} transformation function. Each choice of a transformation function $f$ specifies a learning problem in…

Machine Learning · Computer Science 2011-03-04 Constantinos Daskalakis , Ilias Diakonikolas , Rocco A. Servedio

We study the recovery of functions in various norms, including $L_p$ with $1\le p\le\infty$, based on function evaluations. We obtain worst case error bounds for general classes of functions in terms of the best $L_2$-approximation from a…

Numerical Analysis · Mathematics 2025-12-23 David Krieg , Kateryna Pozharska , Mario Ullrich , Tino Ullrich

Assuming the Generalized Riemann Hypothesis, we provide uniform upper and lower bounds with explicit main terms for $\log{\left|\cL(s)\right|}$ for $\sigma \in (1/2,1)$ and for functions in the Selberg class. In particular, we focus on the…

Number Theory · Mathematics 2025-05-06 Neea Palojärvi , Aleksander Simonič

In this paper, we study lower order terms of the $1$-level density of low-lying zeros of quadratic Hecke L-functions in the Gaussian field. Assuming the Generalized Riemann Hypothesis, our result is valid for even test functions whose…

Number Theory · Mathematics 2023-03-07 Peng Gao , Liangyi Zhao

This note is an attempt to unconditionally prove the existence of weak one way functions (OWF). Starting from a provably intractable decision problem $L_D$ (whose existence is nonconstructively assured from the well-known discrete…

Computational Complexity · Computer Science 2023-07-19 Stefan Rass

Let $\mathbb{F}_q$ be a finite field, and let $F \in \mathbb{F}_q [X]$ be a polynomial with $d = \text{deg} \left( F \right)$ such that $\gcd \left( d, q \right) = 1$. In this paper we prove that the $c$-Boomerang uniformity, $c \neq 0$, of…

Algebraic Geometry · Mathematics 2025-10-22 Matthias Johann Steiner

Benjamini, Yadin, and Yehudayoff (2007) showed that if the maximum degree of a graph $G$ is 'sub-logarithmic,' then the typical range of random $\mathbb Z$-homomorphisms is super-constant. Furthermore, they showed that there is a sharp…

Probability · Mathematics 2024-11-15 Senem Işık , Jinyoung Park

Over the past decade, the low-degree heuristic has been used to estimate the algorithmic thresholds for a wide range of average-case planted vs null distinguishing problems. Such results rely on the hypothesis that if the low-degree moments…

Computational Complexity · Computer Science 2026-01-12 Jun-Ting Hsieh , Daniel M. Kane , Pravesh K. Kothari , Jerry Li , Sidhanth Mohanty , Stefan Tiegel

We prove a $k^{-\Omega(\log(\varepsilon_2 - \varepsilon_1))}$ lower bound for adaptively testing whether a Boolean function is $\varepsilon_1$-close to or $\varepsilon_2$-far from $k$-juntas. Our results provide the first superpolynomial…

Data Structures and Algorithms · Computer Science 2023-04-24 Xi Chen , Shyamal Patel
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