Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
TabFlex: Scaling Tabular Learning to Millions with Linear Attention
Authors: Yuchen Zeng, Tuan Dinh, Wonjun Kang, Andreas C Mueller
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive evaluations demonstrate that TABFLEX can achieve over a 2 speedup compared to TABPFN and a 1.5 speedup over XGBoost, outperforming 25 tested baselines in terms of efficiency across a diverse range of datasets. |
| Researcher Affiliation | Collaboration | 1Work done during an internship at the Gray Systems Lab, Microsoft 2University of Wisconsin-Madison 3University of California San Francisco 4Furiosa AI 5Seoul National University 6Gray System Lab, Microsoft. Correspondence to: Andreas C. Müeller <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Conditional Model Selection Input :A dataset D with n instances of d features |
| Open Source Code | Yes | Our code is available at https: //github.com/microsoft/ticl. |
| Open Datasets | Yes | We evaluate TABFLEX s performance and speed across 115 Open ML tabular datasets (Vanschoren et al., 2013). |
| Dataset Splits | Yes | For each dataset, we consider ten different train/test splits, computing the score mean and standard deviation, as well as the total runtime per 1000 instances. |
| Hardware Specification | Yes | Each model is trained on a single Nvidia A100 80GB PCIe GPU. |
| Software Dependencies | No | In our implementation, we adopt a straightforward Py Torch approach to linear attention rather than an HBM-efficient method. We employ the concise two-line implementation presented in Listing 1. In the following lemma, we demonstrate that this straightforward implementation only incurs a marginal increase in HBM accesses and HBM memory usage. |
| Experiment Setup | Yes | Table 6 summarizes the hyperparameters selected for training TABFLEX-S100, TABFLEX-L100, and TABFLEX-H1K. For all three methods, we utilize the same embedding size of 512, consistent with TABPFN. |