102 citations · 393 across the 37 of their papers we have counts for
40 papers
Transfer Learning for Evolving Domains
Ricardo Ribeiro Pereira, Jacopo Bono, Hugo Ferreira +4
Transfer learning explores how to leverage knowledge from various tasks or domains (sources) to enhance predictive performance in related tasks or domains (targets). Typically, tra…
Mind the truncation gap: challenges of learning on dynamic graphs with recurrent architectures
João Bravo, Jacopo Bono, Pedro Saleiro +2
Systems characterized by evolving interactions, prevalent in social, financial, and biological domains, are effectively modeled as continuous-time dynamic graphs (CTDGs). To manage…
Fair-OBNC: Correcting Label Noise for Fairer Datasets
Inês Oliveira e Silva, Sérgio Jesus, Hugo Ferreira +4
Data used by automated decision-making systems, such as Machine Learning models, often reflects discriminatory behavior that occurred in the past. These biases in the training data…
Aequitas Flow: Streamlining Fair ML Experimentation
Sérgio Jesus, Pedro Saleiro, Inês Oliveira e Silva +5
Aequitas Flow is an open-source framework and toolkit for end-to-end Fair Machine Learning (ML) experimentation, and benchmarking in Python. This package fills integration gaps tha…
Cost-Sensitive Learning to Defer to Multiple Experts with Workload Constraints
Jean V. Alves, Diogo Leitão, Sérgio Jesus +5
Learning to defer (L2D) aims to improve human-AI collaboration systems by learning how to defer decisions to humans when they are more likely to be correct than an ML classifier. E…
DiConStruct: Causal Concept-based Explanations through Black-Box Distillation
Ricardo Moreira, Jacopo Bono, Mário Cardoso +3
Model interpretability plays a central role in human-AI decision-making systems. Ideally, explanations should be expressed using human-interpretable semantic concepts. Moreover, th…
FiFAR: A Fraud Detection Dataset for Learning to Defer
Jean V. Alves, Diogo Leitão, Sérgio Jesus +4
Public dataset limitations have significantly hindered the development and benchmarking of learning to defer (L2D) algorithms, which aim to optimally combine human and AI capabilit…
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…
A Case Study on Designing Evaluations of ML Explanations with Simulated User Studies
Ada Martin, Valerie Chen, Sérgio Jesus +1
When conducting user studies to ascertain the usefulness of model explanations in aiding human decision-making, it is important to use real-world use cases, data, and users. Howeve…
Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation
Sérgio Jesus, José Pombal, Duarte Alves +5
Evaluating new techniques on realistic datasets plays a crucial role in the development of ML research and its broader adoption by practitioners. In recent years, there has been a…
LaundroGraph: Self-Supervised Graph Representation Learning for Anti-Money Laundering
Mário Cardoso, Pedro Saleiro, Pedro Bizarro
Anti-money laundering (AML) regulations mandate financial institutions to deploy AML systems based on a set of rules that, when triggered, form the basis of a suspicious alert to b…
FairGBM: Gradient Boosting with Fairness Constraints
André F Cruz, Catarina Belém, Sérgio Jesus +3
Tabular data is prevalent in many high-stakes domains, such as financial services or public policy. Gradient Boosted Decision Trees (GBDT) are popular in these settings due to thei…
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…
On the Importance of Application-Grounded Experimental Design for Evaluating Explainable ML Methods
Kasun Amarasinghe, Kit T. Rodolfa, Sérgio Jesus +6
Most existing evaluations of explainable machine learning (ML) methods rely on simplifying assumptions or proxies that do not reflect real-world use cases; the handful of more robu…
ConceptDistil: Model-Agnostic Distillation of Concept Explanations
João Bento Sousa, Ricardo Moreira, Vladimir Balayan +2
Concept-based explanations aims to fill the model interpretability gap for non-technical humans-in-the-loop. Previous work has focused on providing concepts for specific models (eg…
Weakly Supervised Multi-task Learning for Concept-based Explainability
Catarina Belém, Vladimir Balayan, Pedro Saleiro +1
In ML-aided decision-making tasks, such as fraud detection or medical diagnosis, the human-in-the-loop, usually a domain-expert without technical ML knowledge, prefers high-level c…
Promoting Fairness through Hyperparameter Optimization
André F. Cruz, Pedro Saleiro, Catarina Belém +2
Considerable research effort has been guided towards algorithmic fairness but real-world adoption of bias reduction techniques is still scarce. Existing methods are either metric-…
How can I choose an explainer? An Application-grounded Evaluation of Post-hoc Explanations
Sérgio Jesus, Catarina Belém, Vladimir Balayan +4
There have been several research works proposing new Explainable AI (XAI) methods designed to generate model explanations having specific properties, or desiderata, such as fidelit…
TimeSHAP: Explaining Recurrent Models through Sequence Perturbations
João Bento, Pedro Saleiro, André F. Cruz +2
Although recurrent neural networks (RNNs) are state-of-the-art in numerous sequential decision-making tasks, there has been little research on explaining their predictions. In this…
Teaching the Machine to Explain Itself using Domain Knowledge
Vladimir Balayan, Pedro Saleiro, Catarina Belém +2
Machine Learning (ML) has been increasingly used to aid humans to make better and faster decisions. However, non-technical humans-in-the-loop struggle to comprehend the rationale b…
A Bandit-Based Algorithm for Fairness-Aware Hyperparameter Optimization
André F. Cruz, Pedro Saleiro, Catarina Belém +2
Considerable research effort has been guided towards algorithmic fairness but there is still no major breakthrough. In practice, an exhaustive search over all possible techniques a…
Aequitas: A Bias and Fairness Audit Toolkit
Pedro Saleiro, Benedict Kuester, Loren Hinkson +5
Recent work has raised concerns on the risk of unintended bias in AI systems being used nowadays that can affect individuals unfairly based on race, gender or religion, among other…