7 papers
Safe Fairness Guarantees Without Demographics in Classification: Spectral Uncertainty Set Perspective
Ainhize Barrainkua, Santiago Mazuelas, Novi Quadrianto +1
As automated classification systems become increasingly prevalent, concerns have emerged over their potential to reinforce and amplify existing societal biases. In the light of thi…
Dissecting Performative Prediction: A Comprehensive Survey
Thomas Kehrenberg, Javier Sanguino, Jose A. Lozano +1
The field of performative prediction had its beginnings in 2020 with the seminal paper "Performative Prediction" by Perdomo et al., which established a novel machine learning setup…
Revisiting (Un)Fairness in Recourse by Minimizing Worst-Case Social Burden
Ainhize Barrainkua, Giovanni De Toni, Jose Antonio Lozano +1
Machine learning based predictions are increasingly used in sensitive decision-making applications that directly affect our lives. This has led to extensive research into ensuring…
Strategically Deceptive Model Deployment in Performative Prediction
Javier Sanguino Bautiste, Thomas Kehrenberg, Jose A. Lozano +1
Machine Learning systems are increasingly deployed in decision-making settings that shape user behavior and, in turn, the data on which future decisions are based. Performative Pre…
Visual-Word Tokenizer: Beyond Fixed Sets of Tokens in Vision Transformers
Leonidas Gee, Wing Yan Li, Viktoriia Sharmanska +1
The cost of deploying vision transformers increasingly represents a barrier to wider industrial adoption. Existing compression techniques require additional end-to-end fine-tuning…
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…