14 citations · 24 across the 5 of their papers we have counts for
5 papers
Gradient-based Counterfactual Explanations using Tractable Probabilistic Models
Xiaoting Shao, Kristian Kersting
Counterfactual examples are an appealing class of post-hoc explanations for machine learning models. Given input of class , its counterfactual is a contrastive example $x^…
Right for the Right Latent Factors: Debiasing Generative Models via Disentanglement
Xiaoting Shao, Karl Stelzner, Kristian Kersting
A key assumption of most statistical machine learning methods is that they have access to independent samples from the distribution of data they encounter at test time. As such, th…
Making deep neural networks right for the right scientific reasons by interacting with their explanations
Patrick Schramowski, Wolfgang Stammer, Stefano Teso +5
Deep neural networks have shown excellent performances in many real-world applications. Unfortunately, they may show "Clever Hans"-like behavior -- making use of confounding factor…
Neural-Symbolic Argumentation Mining: an Argument in Favor of Deep Learning and Reasoning
Andrea Galassi, Kristian Kersting, Marco Lippi +2
Deep learning is bringing remarkable contributions to the field of argumentation mining, but the existing approaches still need to fill the gap toward performing advanced reasoning…
Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures
Xiaoting Shao, Alejandro Molina, Antonio Vergari +4
Probabilistic graphical models are a central tool in AI; however, they are generally not as expressive as deep neural models, and inference is notoriously hard and slow. In contras…