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
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Journal ArticleDOI
A Network Analysis Model for Disambiguation of Names in Lists
TL;DR: This paper proposes an alternative similarity metric based on the probability of walking from one ambiguous name to another in a random walk of the social network constructed from all documents and demonstrates random walks achieve a significant increase in disambiguation capability in comparison to prior models.
Book ChapterDOI
Care and Feeding of Topic Models: Problems, Diagnostics, and Improvements
Edoardo M. Airoldi,David M. Blei,Elena A. Erosheva,Stephen E. Fienberg,Jordan Boyd-Graber,David Mimno,David Newman +6 more
TL;DR: Care and Feeding of Topic Models: Problems, Diagnostics, and Improvements and the Handbook of Mixed Membership Models and Their Applications.
Journal ArticleDOI
Assessing the Impact of Granular Privacy Controls on Content Sharing and Disclosure on Facebook
TL;DR: Results show that Facebook users, on average, increase use of wall posts and decrease use of private messages after the introduction of granular privacy controls, and that user-specific factors play crucial roles in shaping users’ varying reactions to the policy change.
Stochastic Block Models of Mixed Membership
TL;DR: A novel “nested” variational inference scheme is developed, which is necessary to successfully perform fast approximate posterior inference in the authors' models of relational data, which combine features of mixed-membership and block models for relational data in a hierarchical Bayesian framework.
Journal ArticleDOI
Asymptotic and finite-sample properties of estimators based on stochastic gradients
Panos Toulis,Edoardo M. Airoldi +1 more
TL;DR: In this article, the authors introduce implicit stochastic gradient descent procedures, which involve parameter updates that are implicitly defined, and provide theoretical analysis of the asymptotic behavior of both standard and implicit gradient descent-based estimators.