3 papers
stat.ML2022
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…
cs.LG2020
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…
stat.ML2018
Tuning Fairness by Balancing Target Labels
Thomas Kehrenberg, Zexun Chen, Novi Quadrianto
The issue of fairness in machine learning models has recently attracted a lot of attention as ensuring it will ensure continued confidence of the general public in the deployment o…