LUCoS: Latent Unsupervised Context Selection for Tabular Foundation Models
Fuente:
arXiv
Saved in:
| Main Authors: | Ipas, Oroel, Gomez-Trenado, Guillermo, Romero-Zaliz, Rocío, Triguero, Isaac |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Semantic-Inductive Attribute Selection for Zero-Shot Learning
by: Herrera-Aranda, Juan Jose, et al.
Published: (2025)
by: Herrera-Aranda, Juan Jose, et al.
Published: (2025)
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)
Robust Tabular Foundation Models
by: Peroni, Matthew, et al.
Published: (2025)
by: Peroni, Matthew, 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)
ContextBench: Modifying Contexts for Targeted Latent Activation
by: Graham, Robert, et al.
Published: (2025)
by: Graham, Robert, et al.
Published: (2025)
Automated Model Selection for Tabular Data
by: Amballa, Avinash, et al.
Published: (2024)
by: Amballa, Avinash, 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)
VACE: Learning Geometrically Structured Representations for Time Series Anomaly Detection
by: Cencillo, Alberto D., et al.
Published: (2026)
by: Cencillo, Alberto D., 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)
TabGen-ICL: Residual-Aware In-Context Example Selection for Tabular Data Generation
by: Fang, Liancheng, et al.
Published: (2025)
by: Fang, Liancheng, et al.
Published: (2025)
Foundations of Structural Causal Models with Latent Selection
by: Chen, Leihao, et al.
Published: (2024)
by: Chen, Leihao, et al.
Published: (2024)
Towards Understanding Layer Contributions in Tabular In-Context Learning Models
by: Balef, Amir Rezaei, et al.
Published: (2025)
by: Balef, Amir Rezaei, et al.
Published: (2025)
Unsupervised Anomaly Detection for Tabular Data Using Noise Evaluation
by: Dai, Wei, et al.
Published: (2024)
by: Dai, Wei, et al.
Published: (2024)
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)
General Purpose Artificial Intelligence Systems (GPAIS): Properties, Definition, Taxonomy, Societal Implications and Responsible Governance
by: Triguero, Isaac, et al.
Published: (2023)
by: Triguero, Isaac, et al.
Published: (2023)
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)
TFM-Retouche: A Lightweight Input-Space Adapter for Tabular Foundation Models
by: Nguyen, Duong, et al.
Published: (2026)
by: Nguyen, Duong, et al.
Published: (2026)
LAVA: Explainability for Unsupervised Latent Embeddings
by: Stresec, Ivan, et al.
Published: (2025)
by: Stresec, Ivan, et al.
Published: (2025)
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)
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)
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)
Subjectivity in Unsupervised Machine Learning Model Selection
by: Chen, Wanyi, et al.
Published: (2023)
by: Chen, Wanyi, et al.
Published: (2023)
Unsupervised Domain Adaptation within Deep Foundation Latent Spaces
by: Kangin, Dmitry, et al.
Published: (2024)
by: Kangin, Dmitry, et al.
Published: (2024)
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)
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)
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)
Orion-Bix: Bi-Axial Attention for Tabular In-Context Learning
by: Bouadi, Mohamed, et al.
Published: (2025)
by: Bouadi, Mohamed, et al.
Published: (2025)
Fast Unsupervised Deep Outlier Model Selection with Hypernetworks
by: Ding, Xueying, et al.
Published: (2023)
by: Ding, Xueying, et al.
Published: (2023)
MultiTab: A Scalable Foundation for Multitask Learning on Tabular Data
by: Sinodinos, Dimitrios, et al.
Published: (2025)
by: Sinodinos, Dimitrios, et al.
Published: (2025)
When Normality Shifts: Risk-Aware Test-Time Adaptation for Unsupervised Tabular Anomaly Detection
by: Huang, Wei, et al.
Published: (2026)
by: Huang, Wei, et al.
Published: (2026)
FedAD-Bench: A Unified Benchmark for Federated Unsupervised Anomaly Detection in Tabular Data
by: Anwar, Ahmed, et al.
Published: (2024)
by: Anwar, Ahmed, et al.
Published: (2024)
Improving LLM Group Fairness on Tabular Data via In-Context Learning
by: Cherepanova, Valeriia, et al.
Published: (2024)
by: Cherepanova, Valeriia, et al.
Published: (2024)
Orion-MSP: Multi-Scale Sparse Attention for Tabular In-Context Learning
by: Bouadi, Mohamed, et al.
Published: (2025)
by: Bouadi, Mohamed, et al.
Published: (2025)
ConTextTab: A Semantics-Aware Tabular In-Context Learner
by: Spinaci, Marco, et al.
Published: (2025)
by: Spinaci, Marco, et al.
Published: (2025)
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)
TabSieve: Explicit In-Table Evidence Selection for Tabular Prediction
by: Wang, Yongyao, et al.
Published: (2026)
by: Wang, Yongyao, et al.
Published: (2026)
Automatic Demonstration Selection for LLM-based Tabular Data Classification
by: Han, Shuchu, et al.
Published: (2025)
by: Han, Shuchu, et al.
Published: (2025)
Similar Items
-
Semantic-Inductive Attribute Selection for Zero-Shot Learning
by: Herrera-Aranda, Juan Jose, et al.
Published: (2025) -
TabICL: A Tabular Foundation Model for In-Context Learning on Large Data
by: Qu, Jingang, et al.
Published: (2025) -
Robust Tabular Foundation Models
by: Peroni, Matthew, et al.
Published: (2025) -
Position: Foundation Models for Tabular Data within Systemic Contexts Need Grounding
by: Klein, Tassilo, et al.
Published: (2025) -
ContextBench: Modifying Contexts for Targeted Latent Activation
by: Graham, Robert, et al.
Published: (2025)