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We investigate the approximability of several classes of real-valued functions by functions of a small number of variables ({\em juntas}). Our main results are tight bounds on the number of variables required to approximate a function…

Data Structures and Algorithms · Computer Science 2015-03-31 Vitaly Feldman , Jan Vondrak

We initiate the study of property testing of submodularity on the boolean hypercube. Submodular functions come up in a variety of applications in combinatorial optimization. For a vast range of algorithms, the existence of an oracle to a…

Data Structures and Algorithms · Computer Science 2010-08-05 C. Seshadhri , Jan Vondrak

We study the problem of testing if a function depends on a small number of linear directions of its input data. We call a function $f$ a linear $k$-junta if it is completely determined by some $k$-dimensional subspace of the input space. In…

Computational Complexity · Computer Science 2018-11-05 Anindya De , Elchanan Mossel , Joe Neeman

This papers considers the junta testing problem in a recently introduced ``relative error'' variant of the standard Boolean function property testing model. In relative-error testing we measure the distance from $f$ to $g$, where $f,g:…

Computational Complexity · Computer Science 2025-04-15 Xi Chen , William Pires , Toniann Pitassi , Rocco A. Servedio

We study the complexity of learning and approximation of self-bounding functions over the uniform distribution on the Boolean hypercube ${0,1}^n$. Informally, a function $f:{0,1}^n \rightarrow \mathbb{R}$ is self-bounding if for every $x…

Machine Learning · Computer Science 2019-06-04 Vitaly Feldman , Pravesh Kothari , Jan Vondrák

A function $f\colon \{-1,1\}^n \to \{-1,1\}$ is a $k$-junta if it depends on at most $k$ of its variables. We consider the problem of tolerant testing of $k$-juntas, where the testing algorithm must accept any function that is…

Data Structures and Algorithms · Computer Science 2016-11-04 Eric Blais , Clément L. Canonne , Talya Eden , Amit Levi , Dana Ron

Submodular and fractionally subadditive (or equivalently XOS) functions play a fundamental role in combinatorial optimization, algorithmic game theory and machine learning. Motivated by learnability of these classes of functions from random…

Data Structures and Algorithms · Computer Science 2015-08-04 Vitaly Feldman , Jan Vondrak

A real-valued set function is (additively) approximately submodular if it satisfies the submodularity conditions with an additive error. Approximate submodularity arises in many settings, especially in machine learning, where the function…

Data Structures and Algorithms · Computer Science 2020-10-08 Flavio Chierichetti , Anirban Dasgupta , Ravi Kumar

We study the problem of maximizing a function that is approximately submodular under a cardinality constraint. Approximate submodularity implicitly appears in a wide range of applications as in many cases errors in evaluation of a…

Data Structures and Algorithms · Computer Science 2024-11-19 Thibaut Horel , Yaron Singer

Given a function f: {0,1}^n \to {0,1}, the f-isomorphism testing problem requires a randomized algorithm to distinguish functions that are identical to f up to relabeling of the input variables from functions that are far from being so. An…

Data Structures and Algorithms · Computer Science 2011-12-30 Eric Blais , Amit Weinstein , Yuichi Yoshida

Leveraging tools of De, Mossel, and Neeman [FOCS, 2019], we show two different results pertaining to the \emph{tolerant testing} of juntas. Given black-box access to a Boolean function $f:\{\pm1\}^{n} \to \{\pm1\}$, we give a $poly(k,…

Data Structures and Algorithms · Computer Science 2021-06-02 Vishnu Iyer , Avishay Tal , Michael Whitmeyer

Many classical problems in theoretical computer science involve norm, even if implicitly; for example, both XOS functions and downward-closed sets are equivalent to some norms. The last decade has seen a lot of interest in designing…

Data Structures and Algorithms · Computer Science 2024-06-24 Thomas Kesselheim , Marco Molinaro , Sahil Singla

We prove that any non-adaptive algorithm that tests whether an unknown Boolean function $f: \{0, 1\}^n\to \{0, 1\}$ is a $k$-junta or $\epsilon$-far from every $k$-junta must make $\widetilde{\Omega}(k^{3/2} / \epsilon)$ many queries for a…

Computational Complexity · Computer Science 2017-04-24 Xi Chen , Rocco A. Servedio , Li-Yang Tan , Erik Waingarten , Jinyu Xie

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

Over the last three decades, function testing has been extensively studied over Boolean, finite fields, and discrete settings. However, to encode the real-world applications more succinctly, function testing over the reals (where the domain…

Data Structures and Algorithms · Computer Science 2026-03-31 Vipul Arora , Arnab Bhattacharyya , Philips George John , Sayantan Sen

Submodular functions are a fundamental object of study in combinatorial optimization, economics, machine learning, etc. and exhibit a rich combinatorial structure. Many subclasses of submodular functions have also been well studied and…

Data Structures and Algorithms · Computer Science 2013-04-19 Nikhil R. Devanur , Shaddin Dughmi , Roy Schwartz , Ankit Sharma , Mohit Singh

A natural problem in high-dimensional inference is to decide if a classifier $f:\mathbb{R}^n \rightarrow \{-1,1\}$ depends on a small number of linear directions of its input data. Call a function $g: \mathbb{R}^n \rightarrow \{-1,1\}$, a…

Computational Complexity · Computer Science 2021-01-14 Anindya De , Elchanan Mossel , Joe Neeman

The problem of tolerant junta testing is a natural and challenging problem which asks if the property of a function having some specified correlation with a $k$-Junta is testable. In this paper we give an affirmative answer to this…

Computational Complexity · Computer Science 2019-04-09 Anindya De , Elchanan Mossel , Joe Neeman

We prove that any submodular function f: {0,1}^n -> {0,1,...,k} can be represented as a pseudo-Boolean 2k-DNF formula. Pseudo-Boolean DNFs are a natural generalization of DNF representation for functions with integer range. Each term in…

Machine Learning · Computer Science 2012-08-14 Sofya Raskhodnikova , Grigory Yaroslavtsev

We extend the work of Narasimhan and Bilmes [30] for minimizing set functions representable as a difference between submodular functions. Similar to [30], our new algorithms are guaranteed to monotonically reduce the objective function at…

Data Structures and Algorithms · Computer Science 2013-08-27 Rishabh Iyer , Jeff Bilmes
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