A new research paper proposes compact geometric representations for hierarchical data, particularly useful for machine learning tasks involving Directed Acyclic Graphs (DAGs). The work by You et al. builds upon prior research that faced limitations with deep hierarchies, requiring high dimensions. This paper introduces theoretical guarantees for representing hierarchies with embeddings whose dimensions depend on structural graph parameters, proving constant dimension 3 for directed trees and $O(t \log n)$ for graphs of treewidth $t$. The research also provides matching lower bounds and demonstrates practical applicability on real-world datasets, showing significant dimension reduction in high recall scenarios. AI
IMPACT Introduces theoretical guarantees for compact embeddings in hierarchical data, potentially improving efficiency in ML systems.
RANK_REASON The cluster contains a research paper detailing theoretical advancements in machine learning representations.
Read on arXiv cs.IR (Information Retrieval) →
- alphaXiv
- CatalyzeX Code Finder for Papers
- CORE Recommender
- DagsHub
- Directed Acyclic Graph
- Gotit.pub
- Hugging Face
- Influence Flower
- NeurIPS '25
- ScienceCast
- You et al.
AI-generated summary · Google Gemini · from 2 sources. How we write summaries →
