Matej Zecevic

Mach. Learn., 2024

Diagnostic Reasoning in Natural Language: Computational Model and Application.

[BibT_eX]

[DOI]

CoRR, 2024

χSPN: Characteristic Interventional Sum-Product Networks for Causal Inference in Hybrid Domains.

[BibT_eX]

[DOI]

CoRR, 2024

"Do Not Disturb My Circles!" Identifying the Type of Counterfactual at Hand (Short Paper).

[BibT_eX]

[DOI]

Moritz Willig

Proceedings of the Robust Argumentation Machines - First International Conference, 2024

Learning Large DAGs is Harder than you Think: Many Losses are Minimal for the Wrong DAG.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Causal Parrots: Large Language Models May Talk Causality But Are Not Causal.

[BibT_eX]

[DOI]

Trans. Mach. Learn. Res., 2023

Not All Causal Inference is the Same.

[BibT_eX]

[DOI]

Trans. Mach. Learn. Res., 2023

Do Not Marginalize Mechanisms, Rather Consolidate!

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Continual Causal Abstractions.

[BibT_eX]

[DOI]

Proceedings of the AAAI Bridge Program on Continual Causality, 2023

Causal Concept Identification in Open World Environments.

[BibT_eX]

[DOI]

Proceedings of the AAAI Bridge Program on Continual Causality, 2023

Treatment Effect Estimation to Guide Model Optimization in Continual Learning.

[BibT_eX]

[DOI]

Proceedings of the AAAI Bridge Program on Continual Causality, 2023

Continually Updating Neural Causal Models.

[BibT_eX]

[DOI]

Proceedings of the AAAI Bridge Program on Continual Causality, 2023

2022

Pearl Causal Hierarchy on Image Data: Intricacies & Challenges.

[BibT_eX]

[DOI]

CoRR, 2022

On How AI Needs to Change to Advance the Science of Drug Discovery.

[BibT_eX]

[DOI]

Kieran Didi

CoRR, 2022

Can Foundation Models Talk Causality?

[BibT_eX]

[DOI]

CoRR, 2022

Attributions Beyond Neural Networks: The Linear Program Case.

[BibT_eX]

[DOI]

CoRR, 2022

Towards a Solution to Bongard Problems: A Causal Approach.

[BibT_eX]

[DOI]

CoRR, 2022

Tearing Apart NOTEARS: Controlling the Graph Prediction via Variance Manipulation.

[BibT_eX]

[DOI]

CoRR, 2022

Machines Explaining Linear Programs.

[BibT_eX]

[DOI]

CoRR, 2022

Finding Structure and Causality in Linear Programs.

[BibT_eX]

[DOI]

CoRR, 2022

2021

The Causal Loss: Driving Correlation to Imply Causation.

[BibT_eX]

[DOI]

CoRR, 2021

On the Tractability of Neural Causal Inference.

[BibT_eX]

[DOI]

CoRR, 2021

Structural Causal Interpretation Theorem.

[BibT_eX]

[DOI]

Constantin A. Rothkopf

CoRR, 2021

Relating Graph Neural Networks to Structural Causal Models.

[BibT_eX]

[DOI]

CoRR, 2021

Intriguing Parameters of Structural Causal Models.

[BibT_eX]

[DOI]