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TALENT: A Tabular Analytics and Learning Toolbox

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arxiv 2407.04057 v1 pith:YVN2FIJT submitted 2024-07-04 cs.LG

Si-Yang Liu , Hao-Run Cai , Qi-Le Zhou , Han-Jia Ye This is my paper · ORCID
classification cs.LG
keywords methodstabularlearningtoolboxdatadeepdesigntalent
verification ladder T0 review T1 audit T2 compute T3 formal
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Tabular data is one of the most common data sources in machine learning. Although a wide range of classical methods demonstrate practical utilities in this field, deep learning methods on tabular data are becoming promising alternatives due to their flexibility and ability to capture complex interactions within the data. Considering that deep tabular methods have diverse design philosophies, including the ways they handle features, design learning objectives, and construct model architectures, we introduce a versatile deep-learning toolbox called TALENT (Tabular Analytics and LEarNing Toolbox) to utilize, analyze, and compare tabular methods. TALENT encompasses an extensive collection of more than 20 deep tabular prediction methods, associated with various encoding and normalization modules, and provides a unified interface that is easily integrable with new methods as they emerge. In this paper, we present the design and functionality of the toolbox, illustrate its practical application through several case studies, and investigate the performance of various methods fairly based on our toolbox. Code is available at https://github.com/qile2000/LAMDA-TALENT.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Foundation Models for Credit Risk Prediction: A Game Changer?

    cs.LG 2026-05 unverdicted novelty 6.0 of 10

    Tabular foundation models, used zero-shot, match or beat tuned gradient boosting on average in credit PD and LGD benchmarks, with a larger edge on small datasets.

  2. TabPFN Unleashed: A Scalable and Effective Solution to Tabular Classification Problems

    cs.LG 2025-02 conditional novelty 6.0 of 10

    BETA augments TabPFN with encoder fine-tuning and bagging to reduce both bias and variance, achieving SOTA accuracy on 200+ tabular benchmarks while scaling to larger and higher-dimensional data.

  3. Towards Benchmarking Foundation Models for Tabular Data With Text

    cs.LG 2025-07 conditional novelty 5.0 of 10

    A new 13-dataset benchmark shows that adding text embeddings to tabular models usually improves accuracy, but no embedding or downsampling strategy dominates.

  4. Realistic Evaluation of TabPFN v2 in Open Environments

    cs.LG 2025-05 conditional novelty 5.0 of 10

    TabPFN v2 underperforms tree-based models on most open-environment tabular tasks and is only preferable on small, covariate-shifted, class-balanced data.

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