Xiaoting Shao

发表

Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning

Kristian Kersting, Robert Peharz, Zoubin Ghahramani, 2019, UAI.

Making deep neural networks right for the right scientific reasons by interacting with their explanations

Kristian Kersting, Stefano Teso, Anne-Katrin Mahlein, 2020, Nat. Mach. Intell..

Neural-Symbolic Argumentation Mining: An Argument in Favor of Deep Learning and Reasoning

Paolo Torroni, Kristian Kersting, Marco Lippi, 2019, Frontiers in Big Data.

Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures pdf

Kristian Kersting, Thomas Liebig, Robert Peharz, 2019, PGM.

Right for the Right Latent Factors: Debiasing Generative Models via Disentanglement

Karl Stelzner, Xiaoting Shao, K. Kersting, 2022, ArXiv.

Right for Better Reasons: Training Differentiable Models by Constraining their Influence Functions

Kristian Kersting, Xiaoting Shao, Patrick Schramowski, 2021, AAAI.

Towards Understanding and Arguing with Classifiers: Recent Progress

Kristian Kersting, Matthias Thimm, Xiaoting Shao, 2020, Datenbank-Spektrum.

Right for the Wrong Scientific Reasons: Revising Deep Networks by Interacting with their Explanations

K. Kersting, Anne-Katrin Mahlein, Xiaoting Shao, 2020, ArXiv.

Conditional sum-product networks: Modular probabilistic circuits via gate functions

K. Kersting, Robert Peharz, Antonio Vergari, 2021, Int. J. Approx. Reason..

Making deep neural networks right for the right scientific reasons by interacting with their explanations

K. Kersting, Anne-Katrin Mahlein, Xiaoting Shao, 2020, Nature Machine Intelligence.

Independence and D-separation in Abstract Argumentation

Kristian Kersting, Matthias Thimm, Xiaoting Shao, 2020, KR.

Gradient-based Counterfactual Explanations using Tractable Probabilistic Models

K. Kersting, Xiaoting Shao, 2022, ArXiv.