E
Edoardo M. Airoldi
Researcher at Temple University
Publications - 230
Citations - 20370
Edoardo M. Airoldi is an academic researcher from Temple University. The author has contributed to research in topics: Estimator & Inference. The author has an hindex of 50, co-authored 224 publications receiving 18276 citations. Previous affiliations of Edoardo M. Airoldi include Google & Harvard University.
Papers
More filters
Proceedings ArticleDOI
Detecting Network Effects: Randomizing Over Randomized Experiments
Martin Saveski,Jean Pouget-Abadie,Guillaume Saint-Jacques,Weitao Duan,Souvik Ghosh,Ya Xu,Edoardo M. Airoldi +6 more
TL;DR: A new experimental design is leverage for testing whether SUTVA holds, without making any assumptions on how treatment effects may spill over between the treatment and the control group, and the proposed methodology can be applied to settings in which a network is not necessarily observed but, if available, can be used in the analysis.
BookDOI
Handbook of Mixed Membership Models and Their Applications
TL;DR: This handbook spans more than 20 years of the editors and contributors statistical work in the field and explores the use of the models in various application settings, including survey data, population genetics, text analysis, image processing and annotation, and molecular biology.
Proceedings Article
Summarizing topical content with word frequency and exclusivity
TL;DR: The authors introduce Hierarchical Poisson Convolution (HPC), a model which infers regularized estimates of the differential use of words across topics as well as their frequency within topics.
Proceedings Article
Stochastic blockmodel approximation of a graphon: Theory and consistent estimation
TL;DR: In this paper, a stochastic block model approximation (SBA) of the graphon is proposed to estimate a graphon from a set of observed networks generated from the graph.
Proceedings ArticleDOI
A latent mixed membership model for relational data
TL;DR: This paper proposes a Bayesian model that uses a hierarchy of probabilistic assumptions about the way objects interact with one another in order to learn latent groups, their typical interaction patterns, and the degree of membership of objects to groups.