942 citations · 2.3k across the 50 of their papers we have counts for
56 papers
Diffusion Models in Medical Image Inpainting: Challenges, Solution Taxonomy, and Future Directions
Arthur Dantas Mangussi, Joana Cristo Santos, Ricardo Cardoso Pereira +3
Image inpainting aims to reconstruct missing or corrupted regions of an image while preserving as much as possible, visual and semantic consistency. In medical imaging, this task i…
Fairness-Aware Data Valuation for Supervised Learning
José Pombal, Pedro Saleiro, Mário A. T. Figueiredo +1
Data valuation is a ML field that studies the value of training instances towards a given predictive task. Although data bias is one of the main sources of downstream model unfairn…
Understanding Unfairness in Fraud Detection through Model and Data Bias Interactions
José Pombal, André F. Cruz, João Bravo +3
In recent years, machine learning algorithms have become ubiquitous in a multitude of high-stakes decision-making applications. The unparalleled ability of machine learning algorit…
Human-AI Collaboration in Decision-Making: Beyond Learning to Defer
Diogo Leitão, Pedro Saleiro, Mário A. T. Figueiredo +1
Human-AI collaboration (HAIC) in decision-making aims to create synergistic teaming between human decision-makers and AI systems. Learning to defer (L2D) has been presented as a pr…
Prisoners of Their Own Devices: How Models Induce Data Bias in Performative Prediction
José Pombal, Pedro Saleiro, Mário A. T. Figueiredo +1
The unparalleled ability of machine learning algorithms to learn patterns from data also enables them to incorporate biases embedded within. A biased model can then make decisions…
Differentiable Causal Discovery Under Latent Interventions
Gonçalo R. A. Faria, André F. T. Martins, Mário A. T. Figueiredo
Recent work has shown promising results in causal discovery by leveraging interventional data with gradient-based methods, even when the intervened variables are unknown. However,…