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Jonathan Niles-Weed

4 papers hereh-index 320 citations5 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author1
  • last author3

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • math.ST3
  • stat.ML1
same name
  • Jonathan Niles-Weed — 13 papers, h 8
  • Jonathan Niles-Weed — 11 papers, h 7
  • Jonathan Niles-Weed — 10 papers, h 9
  • Jonathan Niles-Weed — 6 papers, h 3
  • Jonathan Niles-Weed — 4 papers, h 2
  • Jonathan Niles-Weed — 4 papers, h 3

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20222025
most citedAsymptotic confidence sets for random linear programs

1 citations · 1 across the 3 of their papers we have counts for

collaborators

4 papers

math.ST2025

Beyond entropic regularization: Debiased Gaussian estimators for discrete optimal transport and general linear programs

Shuyu Liu, Florentina Bunea, Jonathan Niles-Weed

This work proposes new estimators for discrete optimal transport plans that enjoy Gaussian limits centered at the true solution. This behavior stands in stark contrast with the per…

stat.ML2024

The EM-algorithm and the Method of Moments in Softmax Mixture Models

Xin Bing, Florentina Bunea, Jonathan Niles-Weed +1

Softmax Mixture Models (SMMs) are discrete K-component mixture models for the probabilities of selecting one of p candidate feature vectors X1​,…,Xp​∈RL in h…

math.ST2023★ 1 cited

Asymptotic confidence sets for random linear programs

Shuyu Liu, Florentina Bunea, Jonathan Niles-Weed

Motivated by the statistical analysis of the discrete optimal transport problem, we prove distributional limits for the solutions of linear programs with random constraints. Such l…

math.ST2022

Estimation and inference for the Wasserstein distance between mixing measures in topic models

Xin Bing, Florentina Bunea, Jonathan Niles-Weed

The Wasserstein distance between mixing measures has come to occupy a central place in the statistical analysis of mixture models. This work proposes a new canonical interpretation…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.