Cover Image

Distilling Tabular Foundation Models for Structured Health Data

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Tanna, Aditya, Bouarour, Nassim, Bouadi, Mohamed, Sankarapu, Vinay Kumar, Seth, Pratinav
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Pocket Foundation Models: Distilling TFMs into CPU-Ready Gradient-Boosted Trees
by: Tanna, Aditya, 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)

Ensembling Tabular Foundation Models - A Diversity Ceiling And A Calibration Trap
by: Tanna, Aditya, et al.
Published: (2026)

Orion-Bix: Bi-Axial Attention for Tabular In-Context Learning
by: Bouadi, Mohamed, et al.
Published: (2025)

Orion-MSP: Multi-Scale Sparse Attention for Tabular In-Context Learning
by: Bouadi, Mohamed, et al.
Published: (2025)

TabTune: A Unified Library for Inference and Fine-Tuning Tabular Foundation Models
by: Tanna, Aditya, et al.
Published: (2025)

Shaping the Prior: How Synthetic Task Distributions Determine Tabular Foundation Model Quality
by: Bouadi, Mohamed, et al.
Published: (2026)

Exploring Fine-Tuning for Tabular Foundation Models
by: Tanna, Aditya, et al.
Published: (2026)

Position: Behavioural Assurance Cannot Verify the Safety Claims Governance Now Demands
by: Seth, Pratinav, et al.
Published: (2026)

Bridging the Gap in XAI-Why Reliable Metrics Matter for Explainability and Compliance
by: Seth, Pratinav, et al.
Published: (2025)

Interpretability as Alignment: Making Internal Understanding a Design Principle
by: Sengupta, Aadit, et al.
Published: (2025)

DLBacktrace: A Model Agnostic Explainability for any Deep Learning Models
by: Sankarapu, Vinay Kumar, et al.
Published: (2024)

xai_evals : A Framework for Evaluating Post-Hoc Local Explanation Methods
by: Seth, Pratinav, et al.
Published: (2025)

Interpretability-Aware Pruning for Efficient Medical Image Analysis
by: Malik, Nikita, et al.
Published: (2025)

Forgetting That Sticks: Quantization-Permanent Unlearning via Circuit Attribution
by: Sadhu, Saisab, et al.
Published: (2026)

Beyond KL Divergence: Policy Optimization with Flexible Bregman Divergences for LLM Reasoning
by: Yuan, Rui, et al.
Published: (2026)

Beyond Uniform Credit: Causal Credit Assignment for Policy Optimization
by: Khandoga, Mykola, et al.
Published: (2026)

Microeconomic Foundations of Multi-Agent Learning
by: Helou, Nassim
Published: (2026)

$C$-$ΔΘ$: Circuit-Restricted Weight Arithmetic for Selective Refusal
by: Kasliwal, Aditya, et al.
Published: (2026)

Leveraging Knowlegde Graphs for Interpretable Feature Generation
by: Bouadi, Mohamed, et al.
Published: (2024)

Robust Tabular Foundation Models
by: Peroni, Matthew, et al.
Published: (2025)

TabDPT: Scaling Tabular Foundation Models on Real Data
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)

TabICL: A Tabular Foundation Model for In-Context Learning on Large Data
by: Qu, Jingang, et al.
Published: (2025)

MultiTab: A Scalable Foundation for Multitask Learning on Tabular Data
by: Sinodinos, Dimitrios, et al.
Published: (2025)

Data-Efficient Symbolic Regression via Foundation Model Distillation
by: Ying, Wangyang, et al.
Published: (2025)

Position: Foundation Models for Tabular Data within Systemic Contexts Need Grounding
by: Klein, Tassilo, et al.
Published: (2025)

LUCoS: Latent Unsupervised Context Selection for Tabular Foundation Models
by: Ipas, Oroel, et al.
Published: (2026)

Deep Learning within Tabular Data: Foundations, Challenges, Advances and Future Directions
by: Ren, Weijieying, et al.
Published: (2025)

Wearable Accelerometer Foundation Models for Health via Knowledge Distillation
by: Abbaspourazad, Salar, et al.
Published: (2024)

Automated Model Selection for Tabular Data
by: Amballa, Avinash, et al.
Published: (2024)

Tabular Foundation Models Can Learn Association Rules
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)

OmniTabBench: Mapping the Empirical Frontiers of GBDTs, Neural Networks, and Foundation Models for Tabular Data at Scale
by: Jiang, Dihong, et al.
Published: (2026)

Generating Synthetic Relational Tabular Data via Structural Causal Models
by: Hoppe, Frederik, et al.
Published: (2025)

TFM-Retouche: A Lightweight Input-Space Adapter for Tabular Foundation Models
by: Nguyen, Duong, et al.
Published: (2026)

Auditing and Fixing Economic Validity in Tabular Foundation Models for Discrete Choice
by: Wang, Yingshuo, et al.
Published: (2026)

Generating Realistic Tabular Data with Large Language Models
by: Nguyen, Dang, et al.
Published: (2024)

Comparing Task-Agnostic Embedding Models for Tabular Data
by: Hoppe, Frederik, et al.
Published: (2025)

TabularQGAN: A Quantum Generative Model for Tabular Data
by: Bhardwaj, Pallavi, et al.
Published: (2025)