# Testing k-Monotonicity

arXiv: https://arxiv.org/abs/1609.00265 · HTML: https://mlsanity.com/paper/1609.00265/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2016.

## Built on by (most cited first, 2 of 2)

- [Testing distributional assumptions of learning algorithms](https://mlsanity.com/paper/2204.07196/) (2022)
- [Agnostic proper learning of monotone functions: beyond the black-box correction barrier](https://mlsanity.com/paper/2304.02700/) (2023)

## Abstract (arXiv)

A Boolean k-monotone function defined over a finite poset domain cal D alternates between the values 0 and 1 at most k times on any ascending chain in cal D. Therefore, k-monotone functions are natural generalizations of the classical monotone functions, which are the 1-monotone functions. Motivated by the recent interest in k-monotone functions in the context of circuit complexity and learning theory, and by the central role that monotonicity testing plays in the context of property testing, we initiate a systematic study of k-monotone functions, in the property testing model. In this model, the goal is to distinguish functions that are k-monotone (or are close to being k-monotone) from functions that are far from being k-monotone. Our results include the following: - We demonstrate a separation between testing k-monotonicity and testing monotonicity, on the hypercube domain 0,1d, for k≥ 3; - We demonstrate a separation between testing and learning on 0,1d, for k=ω(log d): testing k-monotonicity can be performed with 2^O(√ d · log d· log1/varepsilon) queries, while learning k-monotone functions requires 2^Omega(k· √ d·1/varepsilon) queries (Blais et al. (RANDOM 2015)). - We present a tolerant test for functions fcolon[n]d→ 0,1 with complexity independent of n, which makes progress on a problem left open by Berman et al. (STOC 2014). Our techniques exploit the testing-by-learning paradigm, use novel applications of Fourier analysis on the grid [n]d, and draw connections to distribution testing techniques.