11 citations · 70 across the 22 of their papers we have counts for
7 papers · 1 filter
A Nonparametric Bayesian Model for Sparse Dynamic Multigraphs
Elahe Ghalebi, Hamidreza Mahyar, Radu Grosu +2
As the availability and importance of temporal interaction data--such as email communication--increases, it becomes increasingly important to understand the underlying structure th…
Avoiding Resentment Via Monotonic Fairness
Guy W. Cole, Sinead A. Williamson
Classifiers that achieve demographic balance by explicitly using protected attributes such as race or gender are often politically or culturally controversial due to their lack of…
Sequential Edge Clustering in Temporal Multigraphs
Elahe Ghalebi, Hamidreza Mahyar, Radu Grosu +2
Interaction graphs, such as those recording emails between individuals or transactions between institutions, tend to be sparse yet structured, and often grow in an unbounded manner…
Sequential Gaussian Processes for Online Learning of Nonstationary Functions
Michael Minyi Zhang, Bianca Dumitrascu, Sinead A. Williamson +1
Many machine learning problems can be framed in the context of estimating functions, and often these are time-dependent functions that are estimated in real-time as observations ar…
A New Class of Time Dependent Latent Factor Models with Applications
Sinead A. Williamson, Michael Minyi Zhang, Paul Damien
In many applications, observed data are influenced by some combination of latent causes. For example, suppose sensors are placed inside a building to record responses such as tempe…
Stochastic Blockmodels with Edge Information
Guy W. Cole, Sinead A. Williamson
Stochastic blockmodels allow us to represent networks in terms of a latent community structure, often yielding intuitions about the underlying social structure. Typically, this str…
Large-scale Collaborative Filtering with Product Embeddings
Thom Lake, Sinead A. Williamson, Alexander T. Hawk +2
The application of machine learning techniques to large-scale personalized recommendation problems is a challenging task. Such systems must make sense of enormous amounts of implic…