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 retrieval, where relevance is determined by the ancestor-descendant relationships in a Directed Acyclic Graph (DAG). While previous work has shown that valid embeddings exist when the number of descendants is small, these bounds degrade significantly for deep hierarchies, requiring dimensions as large as the total number of nodes. In this paper, we investigate compact reachability embeddings for more general graph...
| Authors | Prashant Gokhale, Piotr Indyk, Yuhao Liu et al. |
|---|---|
| Primary category | stat.ML |
| Published | 16 June 2026 |
| Last updated at source | 16 June 2026 |
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