102 citations · 165 across the 8 of their papers we have counts for
9 papers
ROC-n-reroll: How verifier imperfection affects test-time scaling
Florian E. Dorner, Yatong Chen, André F. Cruz +1
Test-time scaling aims to improve language model performance by leveraging additional compute during inference. Many works have empirically studied techniques such as Best-of-N (Bo…
Evaluating language models as risk scores
André F. Cruz, Moritz Hardt, Celestine Mendler-Dünner
Current question-answering benchmarks predominantly focus on accuracy in realizable prediction tasks. Conditioned on a question and answer-key, does the most likely token match the…
Unprocessing Seven Years of Algorithmic Fairness
André F. Cruz, Moritz Hardt
Seven years ago, researchers proposed a postprocessing method to equalize the error rates of a model across different demographic groups. The work launched hundreds of papers purpo…
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…
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…
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-…
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…
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…