Towards A Polyglot Framework for Factorized ML
Summary: Proposes Trinity, a polyglot framework that writes factorized LA ML logic once and reuses it across languages via GraalVM. Delivers 8x speedups over materialized joins and supports cross-PL workflows, competitive with Morpheus without PL-specific reimplementation. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. David Justo
- 2. Shaoqing Yi
- 3. Lukas Stadler
- 4. Nadia Polikarpova
- 5. Arun Kumar
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,179 | Coresets over Multiple Tables for Feature-rich and Data-efficient Machine Learning | 2023 | VLDB | 4.8078895e-05 |
| 10,976 | StarfishDB: a Query Execution Engine for Relational Probabilistic Programming | 2024 | SIGMOD | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 13 of 13 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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