arXiv Machine Learning
Jul 21
ExMAG: Learning of Maximally Ancestral Graphs
arXiv:2503. 08245v4 Announce Type: replace Abstract: In mixed graphs, there are both directed and bidirected edges.
Towards Complete Causal Explanation with Expert Knowledge
arXiv:2407. 07338v4 Announce Type: replace-cross Abstract: We study the problem of restricting a Markov equivalence class of maximal ancestral graphs (MAGs) to only those MAGs that contain certain edge marks, which we refer to as expert or orientation knowledge.
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arXiv AI
Aug 5
GENESIS: Towards Explainable Causal Discovery
arXiv:2608. 03868v1 Announce Type: cross Abstract: Causal Discovery (CD) from observational data faces two fundamental challenges.
Hugging Face Trending Papers
Jun 29
Grounding LLM Reasoning under Incomplete Graph Evidence
Knowledge graphs can guide large language models (LLMs) reasoning, but the graph seen by a system is usually a retrieved, linked, temporally scoped, and incomplete evidence state rather than a complete account of truth. We develop a theoretical perspective on grounding observable LLM trajectories under such incomplete graph evidence.
arXiv AI
Jul 28
On the Detection of Commutative Factors in Factor Graphs: Necessary and Sufficient Conditions
arXiv:2605. 26908v2 Announce Type: replace Abstract: Exploiting the indistinguishability of objects in a probabilistic graphical model such as a factor graph is key to lifted probabilistic inference algorithms and allows for tractable probabilistic inference problems with respect to domain sizes.
arXiv Machine Learning
Jul 30
High-Order Markov Blanket Discovery via a k-Order Relaxation of the Faithfulness Assumption
arXiv:2607. 26357v1 Announce Type: new Abstract: The problem of learning the graphical Markov blanket (MB) of a variable from data has applications in many areas such as structure learning for Bayesian networks and Markov random fields, causal discovery, and feature selection.
arXiv Machine Learning
Jun 17
Half a Link can Be Enough to Predict a Whole Link: Understanding Generalization in Knowledge Graph Foundation Models
arXiv:2606. 18001v1 Announce Type: new Abstract: Knowledge graph (KG) foundation models (KGFMs) are zero-shot generalizers: trained once, they can predict links on unseen graphs without retraining.
arXiv Machine Learning
Aug 5
GoT-CD: Graph-of-Thoughts Causal Discovery and the Fragility of Post-hoc Path-Specific Fairness Audits
arXiv:2608. 02877v1 Announce Type: new Abstract: Causal discovery recovers directed structure from observational data and is increasingly used in clinical settings to support mechanism reasoning and fairness audits of predictive models.
arXiv AI
Jun 29
RelBall: Relation Ball with Quaternion Rotation for Knowledge Graph Completion
arXiv:2606. 27967v1 Announce Type: new Abstract: Real-world knowledge graphs are often incomplete, lacking many valid facts.
arXiv AI
Jun 6
Generating Graph-Like Logical Rules for Knowledge Graph Reasoning via Diffusion Models
arXiv:2605. 30747v2 Announce Type: replace Abstract: Logical rules constitute a cornerstone of knowledge graph (KG) reasoning, valued for their interpretability and ability to model relational patterns.
arXiv Machine Learning
Jul 2
Characterizing and Identifying Separable Graphical Models
arXiv:2607. 01057v1 Announce Type: cross Abstract: We study a broad class of graphical models whose independencies correspond to vertex separation in mixed graphs with directed, undirected, and bidirected edges, that are capable of encoding independence structures arising from feedback, latent and selection mechanisms.
arXiv AI
Jun 19
Information Lattice Learning as Probabilistic Graphical Model Structure Learning
arXiv:2606. 19366v1 Announce Type: cross Abstract: Information lattice learning (ILL) learns interpretable rules of a signal by alternately projecting the signal onto a partition lattice that encodes a hierarchy of abstractions and lifting selected rules back to the signal domain.
arXiv Machine Learning
Jul 7
Poisson-Gamma Modeling of Inter-Relational Dependencies in Dynamic Knowledge Graphs
arXiv:2607. 02872v1 Announce Type: new Abstract: Dynamic knowledge graphs are ubiquitous in today's AI applications, as we represent molecular structures, social relationships, and language information using these graph models.