44 citations · 91 across the 6 of their papers we have counts for
6 papers
To Trust or Not to Trust a Regressor: Estimating and Explaining Trustworthiness of Regression Predictions
Kim de Bie, Ana Lucic, Hinda Haned
In hybrid human-AI systems, users need to decide whether or not to trust an algorithmic prediction while the true error in the prediction is unknown. To accommodate such settings,…
Diversifying Task-oriented Dialogue Response Generation with Prototype Guided Paraphrasing
Phillip Lippe, Pengjie Ren, Hinda Haned +2
Existing methods for Dialogue Response Generation (DRG) in Task-oriented Dialogue Systems (TDSs) can be grouped into two categories: template-based and corpus-based. The former pre…
FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles
Ana Lucic, Harrie Oosterhuis, Hinda Haned +1
Model interpretability has become an important problem in machine learning (ML) due to the increased effect that algorithmic decisions have on humans. Counterfactual explanations c…
Why Does My Model Fail? Contrastive Local Explanations for Retail Forecasting
Ana Lucic, Hinda Haned, Maarten de Rijke
In various business settings, there is an interest in using more complex machine learning techniques for sales forecasting. It is difficult to convince analysts, along with their s…
Global Aggregations of Local Explanations for Black Box models
Ilse van der Linden, Hinda Haned, Evangelos Kanoulas
The decision-making process of many state-of-the-art machine learning models is inherently inscrutable to the extent that it is impossible for a human to interpret the model direct…
Explaining Predictions from Tree-based Boosting Ensembles
Ana Lucic, Hinda Haned, Maarten de Rijke
Understanding how "black-box" models arrive at their predictions has sparked significant interest from both within and outside the AI community. Our work focuses on doing this by g…