32 citations · 32 across the 6 of their papers we have counts for
6 papers
Causal Models for Growing Networks
Gecia Bravo-Hermsdorff, Lee M. Gunderson, Kayvan Sadeghi
Real-world networks grow over time; statistical models based on node exchangeability are not appropriate. Instead of constraining the structure of the \textit{distribution} of edge…
Bounding Causal Effects with Leaky Instruments
David S. Watson, Jordan Penn, Lee M. Gunderson +3
Instrumental variables (IVs) are a popular and powerful tool for estimating causal effects in the presence of unobserved confounding. However, classical approaches rely on strong a…
Quantifying Human Priors over Social and Navigation Networks
Gecia Bravo-Hermsdorff
Human knowledge is largely implicit and relational -- do we have a friend in common? can I walk from here to there? In this work, we leverage the combinatorial structure of graphs…
The Graph Pencil Method: Mapping Subgraph Densities to Stochastic Block Models
Lee M Gunderson, Gecia Bravo-Hermsdorff, Peter Orbanz
In this work, we describe a method that determines an exact map from a finite set of subgraph densities to the parameters of a stochastic block model (SBM) matching these densities…
Private and Communication-Efficient Algorithms for Entropy Estimation
Gecia Bravo-Hermsdorff, Róbert Busa-Fekete, Mohammad Ghavamzadeh +2
Modern statistical estimation is often performed in a distributed setting where each sample belongs to a single user who shares their data with a central server. Users are typicall…
A Unifying Framework for Spectrum-Preserving Graph Sparsification and Coarsening
Gecia Bravo-Hermsdorff, Lee M. Gunderson
How might one "reduce" a graph? That is, generate a smaller graph that preserves the global structure at the expense of discarding local details? There has been extensive work on b…