arXiv AI
Aug 12
TACTICL: Task-Aware Compression of Tabular ICL Models
arXiv:2608. 10837v1 Announce Type: cross Abstract: The strong performance of foundation models for tabular tasks comes at substantial inference costs.
Memory Efficient Tabular Foundation Models
arXiv:2607. 27546v1 Announce Type: new Abstract: Tabular Foundation Models, such as TabPFN, have received a large amount of recent attention due to their performance on in-context tabular machine learning tasks, which often exceeds classical baselines.
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arXiv Machine Learning
Aug 4
TabDPT-Turbo: Efficient In-Context Learning for Tabular Prediction
arXiv:2608. 01400v1 Announce Type: new Abstract: Tabular foundation models, driven by in-context learning, have rapidly grown in quality and popularity.
arXiv Machine Learning
Jun 4
Towards Pretraining Text Encoders for TabPFN
arXiv:2606. 04876v1 Announce Type: new Abstract: Tabular foundation models, such as TabPFN, achieve strong performance on tabular datasets with numerical and categorical data, but do not natively handle high-cardinality text features.
arXiv Machine Learning
1d ago
Localized TabICLv2: Scaling Tabular In-Context Learning through k-NN
arXiv:2608. 16429v1 Announce Type: new Abstract: Foundational models for tabular data have made significant progress in recent years, with TabICLv2 reporting state-of-the-art performance on several tabular classification tasks.
arXiv Machine Learning
5d ago
Balanced Adaptive Prototype Selection for Scalable TabPFN Inference on Large-Scale Tabular Data
arXiv:2608. 12989v1 Announce Type: new Abstract: Pretrained tabular foundation models have demonstrated strong predictive capability; however, their application to large-scale datasets remains constrained by the limited inference context.
arXiv Machine Learning
Jun 2
TabPrep: Closing the Feature Engineering Gap in Tabular Benchmarks
arXiv:2606. 02384v1 Announce Type: new Abstract: Progress in tabular machine learning has largely focused on increasingly sophisticated model architectures.
arXiv Machine Learning
Jun 8
TabSwift: An Efficient Tabular Foundation Model with Row-Wise Attention
arXiv:2606. 07345v1 Announce Type: new Abstract: Tabular foundation models, exemplified by TabPFN, perform prediction via in-context learning, inferring test labels directly from labeled training examples.
arXiv Machine Learning
Jun 5
No Need to Train Your RDB Foundation Model
arXiv:2602. 13697v2 Announce Type: replace-cross Abstract: Relational databases (RDBs) contain vast amounts of heterogeneous tabular information that can be exploited for predictive modeling purposes.
arXiv AI
Jun 30
Beyond IID: How General Are Tabular Foundation Models, Really?
arXiv:2606. 30410v1 Announce Type: cross Abstract: Foundation models for predictive machine learning on tabular data have recently gained significant traction in academia and industry.
arXiv Machine Learning
Jun 8
pTNAS: Progressive Neural Architecture Search for Tabular Data
arXiv:2403. 10318v3 Announce Type: replace Abstract: Recent advances have shifted the paradigm of tabular learning toward tabular foundation models, yet their accuracy relies on a heavy inference cost that scales poorly with context size.
arXiv Machine Learning
Jun 3
Speedrunning Tabular Foundation Model Pretraining
arXiv:2606. 03681v1 Announce Type: new Abstract: Pretraining cost is a major bottleneck for research on tabular foundation models, slowing the iteration cycle for new architectures, priors, and optimization ideas.
arXiv Machine Learning
Jun 5
GOTabPFN: From Feature Ordering to Compact Tokenization for Tabular Foundation Models on High-Dimensional Data
arXiv:2606. 05441v1 Announce Type: new Abstract: We investigate how to make small tabular foundation models effective for High-Dimensional, Low-Sample Size (HDLSS) tabular prediction without retraining large backbones.