8 citations · 21 across the 34 of their papers we have counts for
4 papers · 1 filter
Promoting Counterfactual Robustness through Diversity
Francesco Leofante, Nico Potyka
Counterfactual explanations shed light on the decisions of black-box models by explaining how an input can be altered to obtain a favourable decision from the model (e.g., when a l…
Towards a Theory of Faithfulness: Faithful Explanations of Differentiable Classifiers over Continuous Data
Nico Potyka, Xiang Yin, Francesca Toni
There is broad agreement in the literature that explanation methods should be faithful to the model that they explain, but faithfulness remains a rather vague term. We revisit fait…
Learning Gradual Argumentation Frameworks using Genetic Algorithms
Jonathan Spieler, Nico Potyka, Steffen Staab
Gradual argumentation frameworks represent arguments and their relationships in a weighted graph. Their graphical structure and intuitive semantics makes them a potentially interes…
Pseudo-Riemannian Graph Convolutional Networks
Bo Xiong, Shichao Zhu, Nico Potyka +3
Graph convolutional networks (GCNs) are powerful frameworks for learning embeddings of graph-structured data. GCNs are traditionally studied through the lens of Euclidean geometry.…