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New research offers compact geometric representations for hierarchical data

By PulseAugur Editorial · [2 sources] ·

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) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New research offers compact geometric representations for hierarchical data

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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Prashant Gokhale, Piotr Indyk, Yuhao Liu, Sandeep Silwal, Tony Chang Wang, Haike Xu ·

    Compact Geometric Representations of Hierarchies

    arXiv:2606.18520v1 Announce Type: cross Abstract: Computing geometric representations of data is a cornerstone of modern machine learning, typically achieved by training dual encoders which map queries and documents into a shared embedding space. Recent work of You et al. [NeurIP…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Haike Xu ·

    Compact Geometric Representations of Hierarchies

    Computing geometric representations of data is a cornerstone of modern machine learning, typically achieved by training dual encoders which map queries and documents into a shared embedding space. Recent work of You et al. [NeurIPS '25] has extended this approach to hierarchical …

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