2 citations · 2 across the 2 of their papers we have counts for
3 papers
stat.ME2024
Post-selection inference for quantifying uncertainty in changes in variance
Rachel Carrington, Paul Fearnhead
Quantifying uncertainty in detected changepoints is an important problem. However it is challenging as the naive approach would use the data twice, first to detect the changes, and…
stat.ME2023★ 2 cited
Improving Power by Conditioning on Less in Post-selection Inference for Changepoints
Rachel Carrington, Paul Fearnhead
Post-selection inference has recently been proposed as a way of quantifying uncertainty about detected changepoints. The idea is to run a changepoint detection algorithm, and then…
stat.ML2019
Invariance and identifiability issues for word embeddings
Rachel Carrington, Karthik Bharath, Simon Preston
Word embeddings are commonly obtained as optimizers of a criterion function of a text corpus, but assessed on word-task performance using a different evaluation function of…