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cs.LG2022★ 1 cited
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^…
cs.LG2022★ 1 cited
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…
cs.LG2019
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…