10 citations · 13 across the 6 of their papers we have counts for
9 papers
A Survey on Preserving Fairness Guarantees in Changing Environments
Ainhize Barrainkua, Paula Gordaliza, Jose A. Lozano +1
Human lives are increasingly being affected by the outcomes of automated decision-making systems and it is essential for the latter to be, not only accurate, but also fair. The lit…
Okapi: Generalising Better by Making Statistical Matches Match
Myles Bartlett, Sara Romiti, Viktoriia Sharmanska +1
We propose Okapi, a simple, efficient, and general method for robust semi-supervised learning based on online statistical matching. Our method uses a nearest-neighbours-based match…
Addressing Missing Sources with Adversarial Support-Matching
Thomas Kehrenberg, Myles Bartlett, Viktoriia Sharmanska +1
When trained on diverse labeled data, machine learning models have proven themselves to be a powerful tool in all facets of society. However, due to budget limitations, deliberate…
Null-sampling for Interpretable and Fair Representations
Thomas Kehrenberg, Myles Bartlett, Oliver Thomas +1
We propose to learn invariant representations, in the data domain, to achieve interpretability in algorithmic fairness. Invariance implies a selectivity for high level, relevant co…
Contrastive Examples for Addressing the Tyranny of the Majority
Viktoriia Sharmanska, Lisa Anne Hendricks, Trevor Darrell +1
Computer vision algorithms, e.g. for face recognition, favour groups of individuals that are better represented in the training data. This happens because of the generalization tha…
An empirical, Bayesian approach to modelling the impact of weather on crop yield: maize in the US
Raphael Shirley, Edward Pope, Myles Bartlett +8
We apply an empirical, data-driven approach for describing crop yield as a function of monthly temperature and precipitation by employing generative probabilistic models with param…