90 citations · 98 across the 3 of their papers we have counts for
5 papers
Bayesian Evaluation of Large Language Model Behavior
Rachel Longjohn, Shang Wu, Saatvik Kher +2
It is increasingly important to evaluate how text generation systems based on large language models (LLMs) behave, such as their tendency to produce harmful output or their sensiti…
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…
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…
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…