CausalNeSy Call for papers: Deadline March 6, 2025
10 views
Skip to first unread message
utkar...@knoesis.org
unread,
Jan 6, 2025, 9:27:53 PMJan 6
Reply to author
Sign in to reply to author
Forward
Sign in to forward
Delete
You do not have permission to delete messages in this group
Copy link
Report message
Show original message
Either email addresses are anonymous for this group or you need the view member email addresses permission to view the original message
to causalitychallenge
Call for Papers - CausalNeSy: Workshop on Causal Neuro-symbolic AI @ Extended Semantic Web Conference (ESWC) 2025, Portoroz, Slovania Website: https://sites.google.com/view/causalnesy/========= Highlights ========= Submission Deadline: 6th March 2025The workshop on Causal Neuro-symbolic AI (CausalNeSy) aims to bring together researchers and practitioners from academia and industry to share their experiences and insights on applying causality and neuro-symbolic AI techniques to real-world problems.This workshop is archival and all submitted manuscripts will be peer-reviewed and will be published in CEUR proceedings.=============== Topics of interest =============== We invite researchers, practitioners, and industry experts to submit original research papers, surveys, and case studies addressing the following themes (including but not limited to):1. Core Methods and Frameworks
Causal Knowledge Representation: Approaches for representing causal knowledge using neuro-symbolic AI methods.
Causal Reasoning in Neuro-symbolic Systems: Methods for implementing causal reasoning within neuro- symbolic frameworks.
Neuro-symbolic Methods for Causal Structure Learning: Techniques for learning causal structures within neuro- symbolic frameworks.
Causal Representation Learning: Approaches to causal representation learning using neuro-symbolic AI.
2. Integration of Techniques and Paradigms
Causal Knowledge Graph Embeddings: Utilizing embeddings of causal knowledge for graph completion and causal discovery.
Causal Reasoning and Neural Networks: Techniques for harmonizing causal symbolic reasoning with neural net- works to enhance AI interpretability and robustness.
Integration of Causality, Logic, and Probability: Approaches that combine causality, logic, and probabilities in neuro-symbolic AI.
Causal Generative Models: Development and application of causal generative models for machine learning.
Causal Neuro-Symbolic AI in Large Language Models (LLMs): Integration of causality in LLMs for enhanced reasoning capabilities.
Causal Foundation Models: Development of causal foundation models within neuro-symbolic AI.
3. Explanation, Trust, Fairness, and Accountability
Neuro-symbolic Methods for Causal Explanation: Techniques for explaining causes and their effects using neuro-symbolic methods.
Fairness, Accountability, Transparency, and Explainability: Ensuring fairness, accountability, transparency, and explainability in Causal Neuro-symbolic AI systems.
Trustworthiness, Grounding, Instruct-ability, and Alignment: Addressing issues related to the trustworthiness, grounding, instruct-ability, and alignment of Causal Neuro-symbolic AI systems.
4. Applications
Causal Discovery in Complex Environments: Strategies for discovering causal relationships in complex environments using neuro-symbolic AI.
Causal Neuro-symbolic AI in Use: Real-world applications of causal and neuro-symbolic AI methods in domains such as healthcare, finance, autonomous systems, natural language processing, etc
=========== Submissions =========== We invite authors to submit unpublished original papers. Submitted papers should not have been previously published or accepted for publication in substantially similar form in any peer-reviewed venue, such as journals, conferences, or workshops. More information is available at https://sites.google.com/view/causalnesy/============== Important Dates ==============