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Research questionHow can general-purpose tabular predictors balance adaptation-free accuracy with computational cost?Tabular tasks vary substantially in feature structure and target relationships, making it difficult for one predictor to generalize without task-specific adaptation. Increasing model capacity or allocating more computation may improve predictions while reducing practical efficiency.
AI
Evaluation & Benchmarks
Inference Optimization
Machine Learning
Statistical Machine Learning
Latest papersRecent research connected to this question, newest first.Xiaomi-TabLDM: A Tabular Foundation Model Technical ReportThe source presents an in-context tabular predictor pretrained exclusively on synthetic structural-causal-model data and evaluates it on several tabular benchmark suites. It also examines additional test-time computation; the evidence is limited to the reported tasks, pretraining setup, and cost comparisons.research paper · Sep 4, 2026
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