# Smoothing complex-valued signals on Graphs with Monte-Carlo

arXiv: https://arxiv.org/abs/2210.08014 · HTML: https://mlsanity.com/paper/2210.08014/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2022.

## Built on by (most cited first, 1 of 1)

- [Bypassing orthogonalization in the quantum DPP sampler](https://mlsanity.com/paper/2503.05906/) (2025)

## Abstract (arXiv)

We introduce new smoothing estimators for complex signals on graphs, based on a recently studied Determinantal Point Process (DPP). These estimators are built from subsets of edges and nodes drawn according to this DPP, making up trees and unicycles, i.e., connected components containing exactly one cycle. We provide a Julia implementation of these estimators and study their performance when applied to a ranking problem.