Abstract:Table learning has recently emerged as an important paradigm at the intersection of database systems and machine learning. However, applying table learning in practice often requires exporting data from databases and building complex external machine learning pipelines, which disrupts the SQL-centric workflow commonly used by database practitioners. We present TLSQL (Table Learning Structured Query Language), a lightweight SQL-like interface for specifying table learning tasks over SQL-centric data systems. TLSQL allows users to declaratively define predictive tasks using three simple constructs: PREDICT VALUE, TRAIN WITH, and VALIDATE WITH. TLSQL specifications are compiled into standard SQL queries executed by the database engine and task descriptions consumed by downstream table learning frameworks. This design allows users to focus on modeling rather than low-level data preparation and pipeline orchestration. Our demonstration shows that TLSQL enables end-to-end multi-table learning workflows while preserving familiar SQL-based data processing environments. Our code is available at this https URL.
From: Zheng Wang [view email]
[v1]
Tue, 20 Jan 2026 16:06:10 UTC (564 KB)
[v2]
Sat, 24 Jan 2026 02:37:40 UTC (564 KB)
[v3]
Thu, 12 Feb 2026 10:00:39 UTC (564 KB)
[v4]
Wed, 12 Aug 2026 14:08:38 UTC (408 KB)
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