arXiv Machine Learning August 21, 2026 By Seungeun Lee, Kihwan Lee, Subin Bae, Sangjun Lee, Seulbin Lee, Julia Stoyanovich, Il-Youp Kwak, Seungsang Oh

Table2Image: Lightweight Tabular Learning with Generated Proxy Representations and Reliability Diagnostics

Read the original on arXiv Machine Learning →

arXiv:2412. 06265v3 Announce Type: replace Abstract: Deep tabular models should ideally balance predictive performance, parameter efficiency, and robustness to imperfect learning signals---properties that are rarely considered jointly.

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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.

By Andrej Tschalzev, Nick Erickson, Yuyang Wang, Huzefa Rangwala, Stefan L\"udtke, Heiner Stuckenschmidt, Christian Bartelt

benchmarks

arXiv Machine Learning

Jun 9

ReTabSyn: Realistic Tabular Data Synthesis via Reinforcement Learning

arXiv:2603. 10823v2 Announce Type: replace-cross Abstract: Deep generative models can help with data scarcity and privacy by producing synthetic training data, but they struggle in low-data, imbalanced tabular settings to fully learn the complex data distribution.

By Xiaofeng Lin, Seungbae Kim, Zhuoya Li, Zachary DeSoto, Charles Fleming, Guang Cheng

llms reinforcement-learning fine-tuning benchmarks safety

arXiv Machine Learning

Aug 14

TabH2O: A Unified Foundation Model for Tabular Prediction

arXiv:2605. 18383v2 Announce Type: replace Abstract: We present TabH2O, a foundation model for tabular data that performs classification and regression in a single forward pass via in-context learning.

By Pascal Pfeiffer, Dmitry Gordeev, Mathias M\"uller, Laura Fink, Joan Salv\`a Soler, Mark Landry, Branden Murray, Marcos V. Conde, Sri Satish Ambati

llms benchmarks

arXiv Machine Learning

Jun 11

Parameter-Efficient Adapter Tuning for Tabular-Image Multimodal Learning

arXiv:2606. 11682v1 Announce Type: cross Abstract: Tabular-image multimodal learning aims to improve predictive modeling by jointly using structured tabular attributes and visual data.

By Jiaqi Luo

rag fine-tuning multimodal benchmarks

arXiv Machine Learning

Jul 14

TabLoRA: Parameter-Efficient Low-Rank Ensemble Learning for Large-Scale Tabular Data

arXiv:2607. 10077v1 Announce Type: new Abstract: Tabular learning is still dominated by gradient-boosted decision trees (GBDTs), while recent deep learning approaches have become increasingly competitive.

By Jiaqi Luo, Shixin Xu

benchmarks

arXiv AI

Aug 11

iLTM: Integrated Large Tabular Model

arXiv:2511. 15941v2 Announce Type: replace-cross Abstract: Tabular data underpins decisions across science, industry, and public services.

By David Bonet, Mar\c{c}al Comajoan Cara, Alvaro Calafell, Daniel Mas Montserrat, Alexander G. Ioannidis

rag fine-tuning

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Related stories

TabPrep: Closing the Feature Engineering Gap in Tabular Benchmarks

ReTabSyn: Realistic Tabular Data Synthesis via Reinforcement Learning

TabH2O: A Unified Foundation Model for Tabular Prediction

Parameter-Efficient Adapter Tuning for Tabular-Image Multimodal Learning

TabLoRA: Parameter-Efficient Low-Rank Ensemble Learning for Large-Scale Tabular Data

iLTM: Integrated Large Tabular Model