234 citations · 1.2k across the 33 of their papers we have counts for
34 papers
Scalable -d trees for distributed data
Aritra Chakravorty, William S. Cleveland, Patrick J. Wolfe
Data structures known as -d trees have numerous applications in scientific computing, particularly in areas of modern statistics and data science such as range search in decisio…
Distributed estimation through parallel approximants
Aritra Chakravorty, William S. Cleveland, Patrick J. Wolfe
Designing scalable estimation algorithms is a core challenge in modern statistics. Here we introduce a framework to address this challenge based on parallel approximants, which yie…
Robustness on Networks
Marios Papamichalis, Simon Lunagomez, Patrick J. Wolfe
We adopt the statistical framework on robustness proposed by Watson and Holmes in 2016 and then tackle the practical challenges that hinder its applicability to network models. The…
Tractably Modelling Dependence in Networks Beyond Exchangeability
Weichi Wu, Sofia Olhede, Patrick Wolfe
We propose a general framework for modelling network data that is designed to describe aspects of non-exchangeable networks. Conditional on latent (unobserved) variables, the edges…
Modeling Network Populations via Graph Distances
Simón Lunagómez, Sofia C. Olhede, Patrick J. Wolfe
This article introduces a new class of models for multiple networks. The core idea is to parametrize a distribution on labelled graphs in terms of a Fréchet mean graph (which depen…
Evaluating and Optimizing Network Sampling Designs: Decision Theory and Information Theory Perspectives
Simón Lunagómez, Marios Papamichalis, Patrick J. Wolfe +1
Some of the most used sampling mechanisms that implicitly leverage a social network depend on tuning parameters; for instance, Respondent-Driven Sampling (RDS) is specified by the…