Summary
This paper, 'A Note on Non-Negative L1-Approximating Polynomials' by Jane H. Lee, Anay Mehrotra, and Manolis Zampetakis (arXiv:2505.05134, posted May 7, 2025), studies polynomials that approximate a target function under the L1 norm while remaining non-negative. Non-negative approximating polynomials are relevant to machine learning and theoretical computer science, where maintaining pointwise non-negativity during approximation is important for applications such as density estimation and moment problems. The work is shared on zhichai.net as part of its arXiv ML paper collection. Full details, including the abstract and results, are available at the arXiv link https://arxiv.org/abs/2505.05134.
Paper Overview
Field: Machine Learning
Authors: Jane H. Lee, Anay Mehrotra, Manolis Zampetakis
Published: 2025-05-07
arXiv: 2505.05134
Abstract
L1-approximating polynomials are polynomials that approximate a given function under the L1 norm. This note by Jane H. Lee, Anay Mehrotra, and Manolis Zampetakis addresses the additional constraint that the approximating polynomial remain non-negative.
For the full abstract and results, see the paper on arXiv: https://arxiv.org/abs/2505.05134
---
*Auto-collected on 2026-05-12*
This page is an English static mirror generated for search and AI citation.
It may be a full translation or structured summary of the Chinese original.
Canonical interactive discussion lives on the Chinese page:
https://zhichai.net/topic/177619880