12 citations · 24 across the 6 of their papers we have counts for
4 papers · 1 filter
Approximation of group explainers with coalition structure using Monte Carlo sampling on the product space of coalitions and features
Konstandinos Kotsiopoulos, Alexey Miroshnikov, Khashayar Filom +1
In recent years, many Machine Learning (ML) explanation techniques have been designed using ideas from cooperative game theory. These game-theoretic explainers suffer from high com…
On marginal feature attributions of tree-based models
Khashayar Filom, Alexey Miroshnikov, Konstandinos Kotsiopoulos +1
Due to their power and ease of use, tree-based machine learning models, such as random forests and gradient-boosted tree ensembles, have become very popular. To interpret them, loc…
Model-agnostic bias mitigation methods with regressor distribution control for Wasserstein-based fairness metrics
Alexey Miroshnikov, Konstandinos Kotsiopoulos, Ryan Franks +1
This article is a companion paper to our earlier work Miroshnikov et al. (2021) on fairness interpretability, which introduces bias explanations. In the current work, we propose a…
Wasserstein-based fairness interpretability framework for machine learning models
Alexey Miroshnikov, Konstandinos Kotsiopoulos, Ryan Franks +1
The objective of this article is to introduce a fairness interpretability framework for measuring and explaining the bias in classification and regression models at the level of a…