Foundation Models for Structured Data

Structured data (tabular and time-series) underpins high-impact applications across finance, healthcare, enterprise decision-making, and climate modeling. Over the past two years, predictive foundation models tailored to structured data have emerged, enabling in-context learning and transfer across heterogeneous datasets and schemas, challenging the traditional “train per dataset” paradigm. Tabular and time-series foundation models share methodological similarities: pretraining on heterogeneous datasets, in-context learning, and transfer under schema and distribution shift. These similarities create natural synergies across the respective communities. Building on the inaugural Foundation Models for Structured Data workshop at ICML 2025, FMSD @ ICML 2026 will unify the tabular and time-series communities around shared challenges in data curation, scaling, evaluation (including contamination), and real-world deployment (latency, memory, monitoring).