Myles Bartlett — accepted AI-conference papers

2022

Okapi: Generalising Better by Making Statistical Matches Match

Myles Bartlett, Sara Romiti, Viktoriia Sharmanska, Novi Quadrianto

NeurIPS 2022accept

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 matching procedure to generate cross-domain views for a consistency loss, while eliminating statistical outliers. In order to…

Cited by 2 Source ↗PDF ↗code ↗Scholar ↗

2020

Null-sampling for Interpretable and Fair Representations

Thomas Kehrenberg, Myles Bartlett, Oliver Thomas, Novi Quadrianto

ECCV 2020poster

We propose to learn invariant representations, in the data domain, to achieve interpretability in algorithmic fairness. Invariance implies a selectivity for high level, relevant correlations w.r.t. class label annotations, and a robustness to irrelevant correlations with protected characteristics su…

Cited by 33 Source ↗PDF ↗code ↗Scholar ↗