TFM-Retouche: A Lightweight Input-Space Adapter for Tabular Foundation Models
Fuente:
arXiv
Saved in:
| Main Authors: | Nguyen, Duong, Jawhar, Mohammed, Chesneau, Nicolas |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Foundation models for time series forecasting: Application in conformal prediction
by: Achour, Sami, et al.
Published: (2025)
by: Achour, Sami, et al.
Published: (2025)
Robust Tabular Foundation Models
by: Peroni, Matthew, et al.
Published: (2025)
by: Peroni, Matthew, et al.
Published: (2025)
Tabular Data Adapters: Improving Outlier Detection for Unlabeled Private Data
by: Herurkar, Dayananda, et al.
Published: (2025)
by: Herurkar, Dayananda, et al.
Published: (2025)
Measuring and Mitigating Bias for Tabular Datasets with Multiple Protected Attributes
by: Duong, Manh Khoi, et al.
Published: (2024)
by: Duong, Manh Khoi, et al.
Published: (2024)
Distilling Tabular Foundation Models for Structured Health Data
by: Tanna, Aditya, et al.
Published: (2026)
by: Tanna, Aditya, et al.
Published: (2026)
LUCoS: Latent Unsupervised Context Selection for Tabular Foundation Models
by: Ipas, Oroel, et al.
Published: (2026)
by: Ipas, Oroel, et al.
Published: (2026)
TabDPT: Scaling Tabular Foundation Models on Real Data
by: Ma, Junwei, et al.
Published: (2024)
by: Ma, Junwei, et al.
Published: (2024)
In-context Learning of Evolving Data Streams with Tabular Foundational Models
by: Lourenço, Afonso, et al.
Published: (2025)
by: Lourenço, Afonso, et al.
Published: (2025)
Generating Realistic Tabular Data with Large Language Models
by: Nguyen, Dang, et al.
Published: (2024)
by: Nguyen, Dang, et al.
Published: (2024)
Dual-Personalizing Adapter for Federated Foundation Models
by: Yang, Yiyuan, et al.
Published: (2024)
by: Yang, Yiyuan, et al.
Published: (2024)
Ensembling Tabular Foundation Models - A Diversity Ceiling And A Calibration Trap
by: Tanna, Aditya, et al.
Published: (2026)
by: Tanna, Aditya, et al.
Published: (2026)
TabICL: A Tabular Foundation Model for In-Context Learning on Large Data
by: Qu, Jingang, et al.
Published: (2025)
by: Qu, Jingang, et al.
Published: (2025)
Tabular Foundation Models Can Learn Association Rules
by: Karabulut, Erkan, et al.
Published: (2026)
by: Karabulut, Erkan, et al.
Published: (2026)
Is One Layer Enough? Understanding Inference Dynamics in Tabular Foundation Models
by: Balef, Amir Rezaei, et al.
Published: (2026)
by: Balef, Amir Rezaei, et al.
Published: (2026)
BBox-Adapter: Lightweight Adapting for Black-Box Large Language Models
by: Sun, Haotian, et al.
Published: (2024)
by: Sun, Haotian, et al.
Published: (2024)
Lightweight LSTM Model for Energy Theft Detection via Input Data Reduction
by: Collier, Caylum, et al.
Published: (2025)
by: Collier, Caylum, et al.
Published: (2025)
CausalProfiler: Generating Synthetic Benchmarks for Rigorous and Transparent Evaluation of Causal Machine Learning
by: Panayiotou, Panayiotis, et al.
Published: (2025)
by: Panayiotou, Panayiotis, et al.
Published: (2025)
Spectral Adapter: Fine-Tuning in Spectral Space
by: Zhang, Fangzhao, et al.
Published: (2024)
by: Zhang, Fangzhao, et al.
Published: (2024)
TabTune: A Unified Library for Inference and Fine-Tuning Tabular Foundation Models
by: Tanna, Aditya, et al.
Published: (2025)
by: Tanna, Aditya, et al.
Published: (2025)
ECG-RAMBA: Zero-Shot ECG Generalization by Morphology-Rhythm Disentanglement and Long-Range Modeling
by: Nguyen, Hai Duong, et al.
Published: (2025)
by: Nguyen, Hai Duong, et al.
Published: (2025)
FoMo-0D: A Foundation Model for Zero-shot Tabular Outlier Detection
by: Shen, Yuchen, et al.
Published: (2024)
by: Shen, Yuchen, et al.
Published: (2024)
ABBA-Adapters: Efficient and Expressive Fine-Tuning of Foundation Models
by: Singhal, Raghav, et al.
Published: (2025)
by: Singhal, Raghav, et al.
Published: (2025)
MultiTab: A Scalable Foundation for Multitask Learning on Tabular Data
by: Sinodinos, Dimitrios, et al.
Published: (2025)
by: Sinodinos, Dimitrios, et al.
Published: (2025)
Evaluating the Relevance of Uncertainty Estimators for LLM Hallucination
by: Agnimo, Yedidia, et al.
Published: (2026)
by: Agnimo, Yedidia, et al.
Published: (2026)
Shaping the Prior: How Synthetic Task Distributions Determine Tabular Foundation Model Quality
by: Bouadi, Mohamed, et al.
Published: (2026)
by: Bouadi, Mohamed, et al.
Published: (2026)
Auditing and Fixing Economic Validity in Tabular Foundation Models for Discrete Choice
by: Wang, Yingshuo, et al.
Published: (2026)
by: Wang, Yingshuo, et al.
Published: (2026)
Position: Causal Machine Learning Requires Rigorous Synthetic Experiments for Broader Adoption
by: Poinsot, Audrey, et al.
Published: (2025)
by: Poinsot, Audrey, et al.
Published: (2025)
CoRA: Boosting Time Series Foundation Models for Multivariate Forecasting through Correlation-aware Adapter
by: Cheng, Hanyin, et al.
Published: (2026)
by: Cheng, Hanyin, et al.
Published: (2026)
Distributional Regression with Tabular Foundation Models: Evaluating Probabilistic Predictions via Proper Scoring Rules
by: Landsgesell, Jonas, et al.
Published: (2026)
by: Landsgesell, Jonas, et al.
Published: (2026)
Data Presentation Over Architecture: Resampling Strategies for Credit Risk Prediction with Tabular Foundation Models
by: Tanna, Aditya, et al.
Published: (2026)
by: Tanna, Aditya, et al.
Published: (2026)
Graph Adapter of EEG Foundation Models for Parameter Efficient Fine Tuning
by: Suzumura, Toyotaro, et al.
Published: (2024)
by: Suzumura, Toyotaro, et al.
Published: (2024)
Light-Weight Benchmarks Reveal the Hidden Hardware Cost of Zero-Shot Tabular Foundation Models
by: Gangwani, Ishaan, et al.
Published: (2025)
by: Gangwani, Ishaan, et al.
Published: (2025)
IPA: An Information-Reconstructive Input Projection Framework for Efficient Foundation Model Adaptation
by: Yin, Yuan, et al.
Published: (2025)
by: Yin, Yuan, et al.
Published: (2025)
Position: Foundation Models for Tabular Data within Systemic Contexts Need Grounding
by: Klein, Tassilo, et al.
Published: (2025)
by: Klein, Tassilo, et al.
Published: (2025)
Mantis: Lightweight Calibrated Foundation Model for User-Friendly Time Series Classification
by: Feofanov, Vasilii, et al.
Published: (2025)
by: Feofanov, Vasilii, et al.
Published: (2025)
Enabling Causal Discovery in Post-Nonlinear Models with Normalizing Flows
by: Hoang, Nu, et al.
Published: (2024)
by: Hoang, Nu, et al.
Published: (2024)
Deep Learning within Tabular Data: Foundations, Challenges, Advances and Future Directions
by: Ren, Weijieying, et al.
Published: (2025)
by: Ren, Weijieying, et al.
Published: (2025)
OmniTabBench: Mapping the Empirical Frontiers of GBDTs, Neural Networks, and Foundation Models for Tabular Data at Scale
by: Jiang, Dihong, et al.
Published: (2026)
by: Jiang, Dihong, et al.
Published: (2026)
A Lightweight Deep Learning-based Model for Ranking Influential Nodes in Complex Networks
by: Ramadhan, Mohammed A., et al.
Published: (2025)
by: Ramadhan, Mohammed A., et al.
Published: (2025)
Lightweight Time Series Data Valuation on Time Series Foundation Models via In-Context Finetuning
by: Wu, Shunyu, et al.
Published: (2025)
by: Wu, Shunyu, et al.
Published: (2025)
Similar Items
-
Foundation models for time series forecasting: Application in conformal prediction
by: Achour, Sami, et al.
Published: (2025) -
Robust Tabular Foundation Models
by: Peroni, Matthew, et al.
Published: (2025) -
Tabular Data Adapters: Improving Outlier Detection for Unlabeled Private Data
by: Herurkar, Dayananda, et al.
Published: (2025) -
Measuring and Mitigating Bias for Tabular Datasets with Multiple Protected Attributes
by: Duong, Manh Khoi, et al.
Published: (2024) -
Distilling Tabular Foundation Models for Structured Health Data
by: Tanna, Aditya, et al.
Published: (2026)