Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning. - Robert Peharz; Antonio Vergari; Karl Stelzner; Alejandro Molina 001; Martin Trapp 001; Xiaoting Shao; Kristian Kersting; Zoubin Ghahramani (UAI, 2019)

Neural-Symbolic Argumentation Mining: an Argument in Favour of Deep Learning and Reasoning. - Andrea Galassi; Kristian Kersting; Marco Lippi 001; Xiaoting Shao; Paolo Torroni (CoRR, 2019)

Neural-Symbolic Argumentation Mining: An Argument in Favor of Deep Learning and Reasoning. - Andrea Galassi; Kristian Kersting; Marco Lippi 001; Xiaoting Shao; Paolo Torroni (Frontiers Big Data, 2019)

Independence and D-separation in Abstract Argumentation. - Tjitze Rienstra; Matthias Thimm; Kristian Kersting; Xiaoting Shao (KR, 2020)

Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures. - Xiaoting Shao; Alejandro Molina 001; Antonio Vergari; Karl Stelzner; Robert Peharz; Thomas Liebig; Kristian Kersting (PGM, 2020)

Modelling Multivariate Ranking Functions with Min-Sum Networks. - Xiaoting Shao; Zhongjie Yu 001; Arseny Skryagin; Tjitze Rienstra; Matthias Thimm; Kristian Kersting (SUM, 2020)

Right for the Wrong Scientific Reasons: Revising Deep Networks by Interacting with their Explanations. - Patrick Schramowski; Wolfgang Stammer; Stefano Teso; Anna Brugger; Xiaoting Shao; Hans-Georg Luigs; Anne-Katrin Mahlein; Kristian Kersting (CoRR, 2020)

Towards Understanding and Arguing with Classifiers: Recent Progress. - Xiaoting Shao; Tjitze Rienstra; Matthias Thimm; Kristian Kersting (Datenbank-Spektrum, 2020)

Making deep neural networks right for the right scientific reasons by interacting with their explanations. - Patrick Schramowski; Wolfgang Stammer; Stefano Teso; Anna Brugger; Franziska Herbert; Xiaoting Shao; Hans-Georg Luigs; Anne-Katrin Mahlein; Kristian Kersting (Nat. Mach. Intell., 2020)

Right for Better Reasons: Training Differentiable Models by Constraining their Influence Functions. - Xiaoting Shao; Arseny Skryagin; Wolfgang Stammer; Patrick Schramowski; Kristian Kersting (AAAI, 2021)

Right for the Right Latent Factors: Debiasing Generative Models via Disentanglement. - Xiaoting Shao; Karl Stelzner; Kristian Kersting (CoRR, 2022)

Gradient-based Counterfactual Explanations using Tractable Probabilistic Models. - Xiaoting Shao; Kristian Kersting (CoRR, 2022)

Conditional sum-product networks: Modular probabilistic circuits via gate functions. - Xiaoting Shao; Alejandro Molina 001; Antonio Vergari; Karl Stelzner; Robert Peharz; Thomas Liebig; Kristian Kersting (Int. J. Approx. Reason., 2022)

Explaining and Interactively Debugging Deep Models. - Xiaoting Shao (2022)