Image de couverture de livre

Table Foundation Models: on knowledge pre-training for tabular learning

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Kim, Myung Jun, Lefebvre, Félix, Brison, Gaëtan, Perez-Lebel, Alexandre, Varoquaux, Gaël
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Scalable Feature Learning on Huge Knowledge Graphs for Downstream Machine Learning
par: Lefebvre, Félix, et autres
Publié: (2025)

CARTE: Pretraining and Transfer for Tabular Learning
par: Kim, Myung Jun, et autres
Publié: (2024)

Retrieve, Merge, Predict: Augmenting Tables with Data Lakes
par: Cappuzzo, Riccardo, et autres
Publié: (2024)

TabICLv2: A better, faster, scalable, and open tabular foundation model
par: Qu, Jingang, et autres
Publié: (2026)

Decision from Suboptimal Classifiers: Excess Risk Pre- and Post-Calibration
par: Perez-Lebel, Alexandre, et autres
Publié: (2025)

STRABLE: Benchmarking Tabular Machine Learning with Strings
par: Blayer, Gioia, et autres
Publié: (2026)

TabICL: A Tabular Foundation Model for In-Context Learning on Large Data
par: Qu, Jingang, et autres
Publié: (2025)

Imputation for prediction: beware of diminishing returns
par: Morvan, Marine Le, et autres
Publié: (2024)

On the consistency of supervised learning with missing values
par: Josse, Julie, et autres
Publié: (2019)

Extraction of linearized models from pre-trained networks via knowledge distillation
par: Kimura, Fumito, et autres
Publié: (2026)

Survival Models: Proper Scoring Rule and Stochastic Optimization with Competing Risks
par: Alberge, Julie, et autres
Publié: (2024)

Reconfidencing LLMs from the Grouping Loss Perspective
par: Chen, Lihu, et autres
Publié: (2024)

Convex space learning for tabular synthetic data generation
par: Mahendra, Manjunath, et autres
Publié: (2024)

Distributionally robust self-supervised learning for tabular data
par: Ghosh, Shantanu, et autres
Publié: (2024)

Understanding the limitations of self-supervised learning for tabular anomaly detection
par: Mai, Kimberly T., et autres
Publié: (2023)

Information fusion strategy integrating pre-trained language model and contrastive learning for materials knowledge mining
par: Peng, Yongqian, et autres
Publié: (2025)

From pre-training to downstream performance: Does domain-specific pre-training make sense?
par: Krones, Felix
Publié: (2026)

xRFM: Accurate, scalable, and interpretable feature learning models for tabular data
par: Beaglehole, Daniel, et autres
Publié: (2025)

X-TIME: An in-memory engine for accelerating machine learning on tabular data with CAMs
par: Pedretti, Giacomo, et autres
Publié: (2023)

Deep generative models as an adversarial attack strategy for tabular machine learning
par: Dyrmishi, Salijona, et autres
Publié: (2024)

To Each Metric Its Decoding: Post-Hoc Optimal Decision Rules of Probabilistic Hierarchical Classifiers
par: Plaud, Roman, et autres
Publié: (2025)

Causal thinking for decision making on Electronic Health Records: why and how
par: Doutreligne, Matthieu, et autres
Publié: (2023)

Anchor-based oversampling for imbalanced tabular data via contrastive and adversarial learning
par: Mohammadi, Hadi, et autres
Publié: (2025)

Dependency-aware synthetic tabular data generation
par: Umesh, Chaithra, et autres
Publié: (2025)

DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis
par: Petkov, Hristo, et autres
Publié: (2026)

SeBA: Semi-supervised few-shot learning via Separated-at-Birth Alignment for tabular data
par: Jurek, Kacper, et autres
Publié: (2026)

A supervised generative optimization approach for tabular data
par: Nakamura-Sakai, Shinpei, et autres
Publié: (2023)

Post-pre-training for Modality Alignment in Vision-Language Foundation Models
par: Yamaguchi, Shin'ya, et autres
Publié: (2025)

Leveraging Intermediate Representations of Time Series Foundation Models for Anomaly Detection
par: Han, Chan Sik, et autres
Publié: (2025)

Data-efficient pre-training by scaling synthetic megadocs
par: Kim, Konwoo, et autres
Publié: (2026)

MEDS-Tab: Automated tabularization and baseline methods for MEDS datasets
par: Oufattole, Nassim, et autres
Publié: (2024)

Closing the gap on tabular data with Fourier and Implicit Categorical Features
par: Dragoi, Marius, et autres
Publié: (2026)

TREB: a BERT attempt for imputing tabular data imputation
par: Wang, Shuyue, et autres
Publié: (2024)

Woosh: A Sound Effects Foundation Model
par: Hadjeres, Gaëtan, et autres
Publié: (2026)

AutoG: Towards automatic graph construction from tabular data
par: Chen, Zhikai, et autres
Publié: (2025)

TOC-UCO: a comprehensive repository of tabular ordinal classification datasets
par: Ayllón-Gavilán, Rafael, et autres
Publié: (2025)

Targeted synthetic data generation for tabular data via hardness characterization
par: Ferracci, Tommaso, et autres
Publié: (2024)

An interpretable prototype parts-based neural network for medical tabular data
par: Karolczak, Jacek, et autres
Publié: (2026)

Evaluating quality in synthetic data generation for large tabular health datasets
par: Escudié, Jean-Baptiste, et autres
Publié: (2026)

MMM and MMMSynth: Clustering of heterogeneous tabular data, and synthetic data generation
par: Kumari, Chandrani, et autres
Publié: (2023)