activity
20042026
most citedSparsity oracle inequalities for the Lasso

246 citations · 1k across the 29 of their papers we have counts for

collaborators

31 papers

cs.CL2026

Fidelity-Diversity Metrics for Text

Amanda Wang, Tudor Manole, Florentina Bunea +1

As language modeling technology matures, there is an increasing research focus on the composition and curation of datasets used to train these models. For instance, practitioners c…

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 -component mixture models for the probabilities of selecting one of candidate feature vectors 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…

math.ST2021★ 1 cited

Likelihood estimation of sparse topic distributions in topic models and its applications to Wasserstein document distance calculations

Xin Bing, Florentina Bunea, Seth Strimas-Mackey +1

This paper studies the estimation of high-dimensional, discrete, possibly sparse, mixture models in topic models. The data consists of observed multinomial counts of words acro…