1 citations · 2 across the 2 of their papers we have counts for
4 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…
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…