A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is `application/pdf`

.

##
###
Tight Bounds on ℓ 1 Approximation and Learning of Self-Bounding Functions

2017
*
International Conference on Algorithmic Learning Theory
*

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 → R is self-bounding if for every x ∈ {0, 1} n , f (x) upper bounds the sum of all the n marginal decreases in the value of the function at x. Self-bounding functions include such wellknown classes of functions as submodular and fractionally-subadditive (XOS) functions. They were introduced by Boucheron et al. in

dblp:conf/alt/FeldmanKV17
fatcat:veyihsja3vcxnmyxvhju5o7tdy