arXiv Machine Learning
xWhyL: Causal Interactive Learning
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv AI
Jul 10
How Causal Abstraction Underpins Computational Explanation
arXiv:2508. 11214v2 Announce Type: replace-cross Abstract: Explanations of cognitive behavior often appeal to computations over representations.
arXiv AI
Jun 24
An Introduction to Causal Reinforcement Learning
arXiv:2606. 24160v1 Announce Type: new Abstract: Causal inference provides a set of principles and tools that allow one to combine data and knowledge about an environment to reason with questions of counterfactual nature, i.
arXiv AI
Aug 14
A Unifying Perspective on Causal World Models: From Observations to Representations to Structure
arXiv:2608. 13456v1 Announce Type: new Abstract: World Models (WM) are increasingly seen as a foundation for intelligent agents that can predict, plan, and act beyond their training distribution.
arXiv AI
Jun 16
A Definition of Good Explanations and the Challenges Explaining LLM Outputs
arXiv:2606. 14838v1 Announce Type: new Abstract: How to define a good explanation is a long-standing philosophical debate which has found recent renewed interest in the context of AI outputs.
Hugging Face Trending Papers
Jun 23
An Introduction to Causal Reinforcement Learning
Causal inference provides a set of principles and tools that allow one to combine data and knowledge about an environment to reason with questions of counterfactual nature, i. e.
arXiv AI
Aug 25
Reconciling Consistency-Based Diagnosis with Actual-Causality-Based Explanations
The paper establishes connections between Consistency-Based Diagnosis (CBD) and concepts of Actual Causality and Causal Responsibility from the perspective of Explainable AI (XAI). It highlights that CBD has received limited attention within the XAI community and suggests that linking these areas could positively influence both XAI and Explainable Data Management.