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A probability distribution over the Boolean cube is monotone if flipping the value of a coordinate from zero to one can only increase the probability of an element. Given samples of an unknown monotone distribution over the Boolean cube, we…

Data Structures and Algorithms · Computer Science 2020-02-11 Ronitt Rubinfeld , Arsen Vasilyan

We give the first non-trivial upper bounds on the average sensitivity and noise sensitivity of polynomial threshold functions. More specifically, for a Boolean function f on n variables equal to the sign of a real, multivariate polynomial…

Computational Complexity · Computer Science 2014-03-28 Prahladh Harsha , Adam Klivans , Raghu Meka

We show improved monotonicity testers for the Boolean hypercube under the $p$-biased measure, as well as over the hypergrid $[m]^n$. Our results are: 1. For any $p\in (0,1)$, for the $p$-biased hypercube we show a non-adaptive tester that…

Computational Complexity · Computer Science 2022-11-18 Mark Braverman , Subhash Khot , Guy Kindler , Dor Minzer

A function defined on the Boolean hypercube is $k$-Fourier-sparse if it has at most $k$ nonzero Fourier coefficients. For a function $f: \mathbb{F}_2^n \rightarrow \mathbb{R}$ and parameters $k$ and $d$, we prove a strong upper bound on the…

Data Structures and Algorithms · Computer Science 2015-04-08 Ishay Haviv , Oded Regev

We prove two main results on how arbitrary linear threshold functions $f(x) = \sign(w\cdot x - \theta)$ over the $n$-dimensional Boolean hypercube can be approximated by simple threshold functions. Our first result shows that every…

Computational Complexity · Computer Science 2009-10-21 Ilias Diakonikolas , Rocco A. Servedio

We consider the convolution model where i.i.d. random variables $X_i$ having unknown density $f$ are observed with additive i.i.d. noise, independent of the $X$'s. We assume that the density $f$ belongs to either a Sobolev class or a class…

Statistics Theory · Mathematics 2009-09-29 Cristina Butucea

A Boolean function $f:\{0,1\}^n\to \{0,1\}$ is $k$-linear if it returns the sum (over the binary field $F_2$) of $k$ coordinates of the input. In this paper, we study property testing of the classes $k$-Linear, the class of all $k$-linear…

Computational Complexity · Computer Science 2020-06-09 Nader H. Bshouty

In this note we compare two measures of the complexity of a class $\mathcal F$ of Boolean functions studied in (unconditional) pseudorandomness: $\mathcal F$'s ability to distinguish between biased and uniform coins (the coin problem), and…

Computational Complexity · Computer Science 2020-09-01 Rohit Agrawal

Let $T_{\epsilon}$, $0 \le \epsilon \le 1/2$, be the noise operator acting on functions on the boolean cube $\{0,1\}^n$. Let $f$ be a nonnegative function on $\{0,1\}^n$ and let $q \ge 1$. In arXiv:1809.09696 the $\ell_q$ norm of…

Information Theory · Computer Science 2020-10-07 Alex Samorodnitsky

In this note, we develop a bounded-error quantum algorithm that makes $\tilde O(n^{1/4}\varepsilon^{-1/2})$ queries to a Boolean function $f$, accepts a monotone function, and rejects a function that is $\varepsilon$-far from being…

Quantum Physics · Physics 2015-03-11 Aleksandrs Belovs , Eric Blais

In this note we consider Boolean functions defined on the discrete cube equipped with a biased product probability measure. We prove that if the spectrum of such a function is concentrated on the first two Fourier levels, then the function…

Combinatorics · Mathematics 2013-11-14 Piotr Nayar

The $\epsilon$-approximate degree of a Boolean function $f: \{-1, 1\}^n \to \{-1, 1\}$ is the minimum degree of a real polynomial that approximates $f$ to within $\epsilon$ in the $\ell_\infty$ norm. We prove several lower bounds on this…

Computational Complexity · Computer Science 2014-03-25 Mark Bun , Justin Thaler

The relative-error property testing model was introduced in [CDHLNSY24] to facilitate the study of property testing for "sparse" Boolean-valued functions, i.e. ones for which only a small fraction of all input assignments satisfy the…

Data Structures and Algorithms · Computer Science 2026-04-03 Xi Chen , Anindya De , Yizhi Huang , Shivam Nadimpalli , Rocco A. Servedio , Tianqi Yang

This paper studies estimation of and inference on a distribution function $F$ that is concave on the nonnegative half line and admits a density function $f$ with potentially unbounded support. When $F$ is strictly concave, we show that the…

Statistics Theory · Mathematics 2019-11-12 Zheng Fang

We develop a general theory for the goodness-of-fit test to non-linear models. In particular, we assume that the observations are noisy samples of a submanifold defined by a \yao{sufficiently smooth non-linear map}. The observation noise is…

Machine Learning · Statistics 2020-11-12 Alexander Shapiro , Yao Xie , Rui Zhang

This paper studies the classification of high-dimensional Gaussian signals from low-dimensional noisy, linear measurements. In particular, it provides upper bounds (sufficient conditions) on the number of measurements required to drive the…

Information Theory · Computer Science 2016-11-03 Hugo Reboredo , Francesco Renna , Robert Calderbank , Miguel R. D. Rodrigues

We study a natural complexity measure of Boolean functions known as the rational degree. Denoted $\textrm{rdeg}(f)$, it is the minimal degree of a rational function that is equal to $f$ on the Boolean hypercube. For total functions $f$, it…

Computational Complexity · Computer Science 2025-04-16 Vishnu Iyer , Siddhartha Jain , Robin Kothari , Matt Kovacs-Deak , Vinayak M. Kumar , Luke Schaeffer , Daochen Wang , Michael Whitmeyer

Given Boolean functions \( f, g : \mathbb{F}_2^n \to \{-1,+1\} \), we say they are {\em linearly isomorphic} if there exists \( A \in \mathrm{GL}_n(\mathbb{F}_2) \) such that \( f(x)=g(Ax) \) for all \( x \). We study this problem in the…

Computational Complexity · Computer Science 2026-01-14 Swarnalipa Datta , Arijit Ghosh , Chandrima Kayal , Manaswi Paraashar , Manmatha Roy

This paper considers the problem of approximating a Boolean function $f$ using another Boolean function from a specified class. Two classes of approximating functions are considered: $k$-juntas, and linear Boolean functions. The $n$ input…

Information Theory · Computer Science 2019-07-09 Mohsen Heidari , S. Sandeep Pradhan , Ramji Venkataramanan

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