A Similarity Measure Between Functions with Applications to Statistical Learning and Optimization

Chengpiao Huang, Kaizheng Wang
1/14/2025
cs.LGmath.OCstat.ML

No confident code implementation found for this paper. 1 possible match can be shown below.

Abstract

In this note, we present a novel measure of similarity between two functions. It quantifies how the sub-optimality gaps of two functions convert to each other, and unifies several existing notions of functional similarity. We show that it has convenient operation rules, and illustrate its use in empirical risk minimization and non-stationary online optimization.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 1 weaker match is hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Rashomon Alignment

82% match1 repo2026

Functional Risk Minimization

78% match2024

META-SMGO-Δ: similarity as a prior in black-box optimization

77% match2023

Statistical Indistinguishability of Learning Algorithms

76% match2023

Feature Subset Weighting for Distance-based Supervised Learning through Choquet Integration

75% match2025

Statistical Learning under Heterogeneous Distribution Shift

75% match2023

Cite this paper

@article{huang2025similarity,
  title  = {A Similarity Measure Between Functions with Applications to Statistical Learning and Optimization},
  author = {Chengpiao Huang and Kaizheng Wang},
  year   = {2025},
  eprint = {2501.08317},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2501.08317v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2501.08317.svg)](https://codeofpaper.com/paper/2501.08317)

Discussion