Learning Linear Regression Models over Factorized Joins
Summary: Learning linear regression on training data defined by arbitrary joins using factorized representations. Proposes F/FDB, F, F/SQL to factorize cofactors, decouple gradient updates from convergence, and exploit join/union commutativity; factorized joins can be exponentially cheaper, delivering up to 1000x speedups over MADlib, StatsModels, and R. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Maximilian Schleich
- 2. Dan Olteanu
- 3. Radu Ciucanu
Incoming Citations (Sorted by Pagerank)
Showing 6 of 56 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,406 | GES: High-Performance Graph Processing Engine and Service in Huawei | 2025 | SIGMOD | 4.1945683e-05 |
| 10,924 | Improved Approximation Algorithms for Relational Clustering | 2024 | PODS | 4.1945683e-05 |
| 11,220 | Lightweight Materialization for Fast Dashboards Over Joins | 2023 | SIGMOD | 4.1945683e-05 |
| 11,330 | Lower Bounds for Sparse Oblivious Subspace Embeddings | 2022 | PODS | 4.1945683e-05 |
| 11,363 | Givens QR Decomposition over Relational Databases | 2022 | SIGMOD | 4.1945683e-05 |
| 11,676 | doppioDB 2.0: Hardware Techniques for Improved Integration of Machine Learning into Databases | 2019 | VLDB | 4.1945683e-05 |
Outgoing Citations (Sorted by Pagerank)
Showing 15 of 15 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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