Tabular foundation models excel on tiny- to medium-sized IID data but are outperformed by traditional tree-based and deep learning models on non-IID, large, and high-dimensional datasets, based on evaluations across 11 models and 142 datasets in the new BeyondArena benchmark.
arXiv preprintarXiv:2408.14817
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
Clutch accelerates vector-scalar comparisons in PuD systems via chunked temporal coding, delivering 2.9x throughput and 3.0x energy gains over prior bit-serial PuD while also mapping decision tree inference to PuD for the first time.
TabPrep is a new feature engineering pipeline that targets three data patterns and improves performance of tree-based, neural, linear, and foundation models on tabular benchmarks, often more than model architecture changes.
A hierarchical graph neural network predicts commodity futures prices to generate calendar spread positions that outperform benchmarks on CME data by leveraging maturity-dependent correlations.
citing papers explorer
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Beyond IID: How General Are Tabular Foundation Models, Really?
Tabular foundation models excel on tiny- to medium-sized IID data but are outperformed by traditional tree-based and deep learning models on non-IID, large, and high-dimensional datasets, based on evaluations across 11 models and 142 datasets in the new BeyondArena benchmark.
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Clutch: High Performance Vector-Scalar Comparison using DRAM via Chunked Temporal Coding
Clutch accelerates vector-scalar comparisons in PuD systems via chunked temporal coding, delivering 2.9x throughput and 3.0x energy gains over prior bit-serial PuD while also mapping decision tree inference to PuD for the first time.
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TabPrep: Closing the Feature Engineering Gap in Tabular Benchmarks
TabPrep is a new feature engineering pipeline that targets three data patterns and improves performance of tree-based, neural, linear, and foundation models on tabular benchmarks, often more than model architecture changes.
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Hierarchical Graph Learning for Calendar Spread Strategies in Commodity Futures Markets
A hierarchical graph neural network predicts commodity futures prices to generate calendar spread positions that outperform benchmarks on CME data by leveraging maturity-dependent correlations.