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Publications

Displaying results 131 to 140 of 668.

  1. Xiaoting Shao; Kristian Kersting

    Gradient-based Counterfactual Explanations using Tractable Probabilistic Models

    In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2205.07774, Pages 0-10, arXiv, 2022.

  2. Xiaoting Shao; Karl Stelzner; Kristian Kersting

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

    In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2202.00391, Pages 0-10, arXiv, 2022.

  3. Arseny Skryagin; Wolfgang Stammer; Daniel Ochs; Devendra Singh Dhami; Kristian Kersting

    Neural-Probabilistic Answer Set Programming

    In: Gabriele Kern-Isberner; Gerhard Lakemeyer; Thomas Meyer (Hrsg.). Proceedings of the 19th International Conference on Principles of Knowledge Representation and Reasoning. International Conference on Principles of Knowledge Representation and Reasoning (KR-2022), July 31 - August 5, Haifa, Israel, IJCAI Organization, 2022.

  4. Claas Völcker; Alejandro Molina; Johannes Neumann; Dirk Westermann; Kristian Kersting

    DeepNotebooks: Deep Probabilistic Models Construct Python Notebooks for Reporting Datasets

    In: Peggy Cellier; Kurt Driessens (Hrsg.). Machine Learning and Knowledge Discovery in Databases. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD-2019), International Workshops of ECML PKDD 2019, Proceedings, Part I, September 16-20, Würzburg, Germany, Pages 28-43, Communications in Computer and Information Science, Vol. 1167, Springer, 2019.

  5. Navdeep Kaur; Gautam Kunapuli; Saket Joshi; Kristian Kersting; Sriraam Natarajan

    Neural Networks for Relational Data

    In: Dimitar Kazakov; Can Erten (Hrsg.). Inductive Logic Programming - 29th International Conference, Proceedings. International Conference on Inductive Logic Programming (ILP-2019), September 3-5, Plovdiv, Bulgaria, Pages 62-71, Lecture Notes in Computer Science (LNAI), Vol. 11770, Springer, 2019.

  6. Karl Stelzner; Robert Peharz; Kristian Kersting

    Faster Attend-Infer-Repeat with Tractable Probabilistic Models

    In: Kamalika Chaudhuri; Ruslan Salakhutdinov (Hrsg.). Proceedings of the 36th International Conference on Machine Learning. International Conference on Machine Learning (ICML-2019), June 9-15, Long Beach, California, USA, Pages 5966-5975, Proceedings of Machine Learning Research, Vol. 97, PMLR, 2019.

  7. Andrea Galassi; Kristian Kersting; Marco Lippi; Xiaoting Shao; Paolo Torroni

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

    In: Frontiers in Big Data, Vol. 2 - 2019, Pages 0-10, Frontiers, 1/2020.

  8. Kristian Kersting; Miryung Kim; Guy Van den Broeck; Thomas Zimmermann

    SE4ML - Software Engineering for AI-ML-based Systems (Dagstuhl Seminar 20091)

    In: Dagstuhl Reports, Vol. 10, No. 2, Pages 76-87, Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik, 2020.

  9. Patrick Schramowski; Wolfgang Stammer; Stefano Teso; Anna Brugger; Xiaoting Shao; Hans-Georg Luigs; Anne-Katrin Mahlein; Kristian Kersting

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

    In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2001.05371, Pages 0-10, arXiv, 2020.

  10. Xiaoting Shao; Alejandro Molina; Antonio Vergari; Karl Stelzner; Robert Peharz; Thomas Liebig; Kristian Kersting

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

    In: Manfred Jaeger; Thomas Dyhre Nielsen (Hrsg.). Proceedings of the 10th International Conference on Probabilistic Graphical Models. International Conference on Probabilistic Graphical Models (PGM-2020), September 23-25, Aalborg, Denmark, Pages 401-412, Proceedings of Machine Learning Research, Vol. 138, PMLR, 2020.

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