19 citations · 41 across the 5 of their papers we have counts for
8 papers
Dancing in the Shadows: Harnessing Ambiguity for Fairer Classifiers
Ainhize Barrainkua, Paula Gordaliza, Jose A. Lozano +1
This paper introduces a novel approach to bolster algorithmic fairness in scenarios where sensitive information is only partially known. In particular, we propose to leverage insta…
Are Compressed Language Models Less Subgroup Robust?
Leonidas Gee, Andrea Zugarini, Novi Quadrianto
To reduce the inference cost of large language models, model compression is increasingly used to create smaller scalable models. However, little is known about their robustness to…
Uncertainty in Fairness Assessment: Maintaining Stable Conclusions Despite Fluctuations
Ainhize Barrainkua, Paula Gordaliza, Jose A. Lozano +1
Several recent works encourage the use of a Bayesian framework when assessing performance and fairness metrics of a classification algorithm in a supervised setting. We propose the…
RealPatch: A Statistical Matching Framework for Model Patching with Real Samples
Sara Romiti, Christopher Inskip, Viktoriia Sharmanska +1
Machine learning classifiers are typically trained to minimise the average error across a dataset. Unfortunately, in practice, this process often exploits spurious correlations cau…
Gray-box inference for structured Gaussian process models
Pietro Galliani, Amir Dezfouli, Edwin V. Bonilla +1
We develop an automated variational inference method for Bayesian structured prediction problems with Gaussian process (GP) priors and linear-chain likelihoods. Our approach does n…
Learning to Transfer Privileged Information
Viktoriia Sharmanska, Novi Quadrianto, Christoph H. Lampert
We introduce a learning framework called learning using privileged information (LUPI) to the computer vision field. We focus on the prototypical computer vision problem of teaching…