The Role of Feature Interactions in Graph-based Tabular Deep Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Dubbeldam, Elias, Mohammadi, Reza, Schoonhoven, Marit, Birbil, S. Ilker |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Scalable Bayesian Structure Learning for Gaussian Graphical Models Using Marginal Pseudo-likelihood
by: Mohammadi, Reza, et al.
Published: (2023)
by: Mohammadi, Reza, et al.
Published: (2023)
Bayesian Structure Learning in Undirected Gaussian Graphical Models: Literature Review with Empirical Comparison
by: Vogels, Lucas, et al.
Published: (2023)
by: Vogels, Lucas, et al.
Published: (2023)
Multiple Jump MCMC: A Scalable Algorithm for Bayesian Inference on Binary Model Spaces
by: Vogels, Lucas, et al.
Published: (2026)
by: Vogels, Lucas, et al.
Published: (2026)
Modeling Alzheimer's Disease: Bayesian Copula Graphical Model from Demographic, Cognitive, and Neuroimaging Data
by: Vogels, Lucas, et al.
Published: (2024)
by: Vogels, Lucas, et al.
Published: (2024)
Learning An Interpretable Risk Scoring System for Maximizing Decision Net Benefit
by: Chi, Wenhao, et al.
Published: (2026)
by: Chi, Wenhao, et al.
Published: (2026)
Machine Learning for K-adaptability in Two-stage Robust Optimization
by: Julien, Esther, et al.
Published: (2022)
by: Julien, Esther, et al.
Published: (2022)
Learning with Subset Stacking
by: Birbil, Ş. İlker, et al.
Published: (2021)
by: Birbil, Ş. İlker, et al.
Published: (2021)
Coherent Local Explanations for Mathematical Optimization
by: Otto, Daan, et al.
Published: (2025)
by: Otto, Daan, et al.
Published: (2025)
Generating Samples to Probe Trained Models
by: Kıral, Eren Mehmet, et al.
Published: (2025)
by: Kıral, Eren Mehmet, et al.
Published: (2025)
Counterfactual Explanations for Linear Optimization
by: Kurtz, Jannis, et al.
Published: (2024)
by: Kurtz, Jannis, et al.
Published: (2024)
Bolstering Stochastic Gradient Descent with Model Building
by: Birbil, S. Ilker, et al.
Published: (2021)
by: Birbil, S. Ilker, et al.
Published: (2021)
Arithmetic Feature Interaction Is Necessary for Deep Tabular Learning
by: Cheng, Yi, et al.
Published: (2024)
by: Cheng, Yi, et al.
Published: (2024)
Linear Model Extraction via Factual and Counterfactual Queries
by: Otto, Daan, et al.
Published: (2026)
by: Otto, Daan, et al.
Published: (2026)
Improving understanding and trust in AI: How users benefit from interval-based counterfactual explanations
by: Röber, Tabea E., et al.
Published: (2026)
by: Röber, Tabea E., et al.
Published: (2026)
Rule Generation for Classification: Scalability, Interpretability, and Fairness
by: Röber, Tabea E., et al.
Published: (2021)
by: Röber, Tabea E., et al.
Published: (2021)
Iterative Feature Exclusion Ranking for Deep Tabular Learning
by: Shaninah, Fathi Said Emhemed, et al.
Published: (2024)
by: Shaninah, Fathi Said Emhemed, et al.
Published: (2024)
Unveiling the Role of Data Uncertainty in Tabular Deep Learning
by: Kartashev, Nikolay, et al.
Published: (2025)
by: Kartashev, Nikolay, et al.
Published: (2025)
Output-Constrained Decision Trees
by: Tunç, Hüseyin, et al.
Published: (2024)
by: Tunç, Hüseyin, et al.
Published: (2024)
Deep Feature Embedding for Tabular Data
by: Wu, Yuqian, et al.
Published: (2024)
by: Wu, Yuqian, et al.
Published: (2024)
Interpretable Feature Interaction via Statistical Self-supervised Learning on Tabular Data
by: Zhang, Xiaochen, et al.
Published: (2025)
by: Zhang, Xiaochen, et al.
Published: (2025)
Improving Deep Tabular Learning
by: Sarafian, Sivan, et al.
Published: (2025)
by: Sarafian, Sivan, et al.
Published: (2025)
From Features to Structure: Task-Aware Graph Construction for Relational and Tabular Learning with GNNs
by: Cucumides, Tamara, et al.
Published: (2025)
by: Cucumides, Tamara, et al.
Published: (2025)
Graph Neural Network contextual embedding for Deep Learning on Tabular Data
by: Villaizán-Vallelado, Mario, et al.
Published: (2023)
by: Villaizán-Vallelado, Mario, et al.
Published: (2023)
Clinicians' Voice: Fundamental Considerations for XAI in Healthcare
by: Röber, T. E., et al.
Published: (2024)
by: Röber, T. E., et al.
Published: (2024)
TabKD: Tabular Knowledge Distillation through Interaction Diversity of Learned Feature Bins
by: Pereira, Shovon Niverd, et al.
Published: (2026)
by: Pereira, Shovon Niverd, et al.
Published: (2026)
Benchmarking Optimizers for MLPs in Tabular Deep Learning
by: Gorishniy, Yury, et al.
Published: (2026)
by: Gorishniy, Yury, et al.
Published: (2026)
A Survey on Deep Tabular Learning
by: Somvanshi, Shriyank, et al.
Published: (2024)
by: Somvanshi, Shriyank, et al.
Published: (2024)
Tabular and Deep Learning for the Whittle Index
by: Relaño, Francisco Robledo, et al.
Published: (2024)
by: Relaño, Francisco Robledo, et al.
Published: (2024)
TabularBench: Benchmarking Adversarial Robustness for Tabular Deep Learning in Real-world Use-cases
by: Simonetto, Thibault, et al.
Published: (2024)
by: Simonetto, Thibault, et al.
Published: (2024)
Tabular and Deep Reinforcement Learning for Gittins Index
by: Dhankhar, Harshit, et al.
Published: (2024)
by: Dhankhar, Harshit, et al.
Published: (2024)
Mambular: A Sequential Model for Tabular Deep Learning
by: Thielmann, Anton Frederik, et al.
Published: (2024)
by: Thielmann, Anton Frederik, et al.
Published: (2024)
On the Efficiency of NLP-Inspired Methods for Tabular Deep Learning
by: Thielmann, Anton Frederik, et al.
Published: (2024)
by: Thielmann, Anton Frederik, et al.
Published: (2024)
TFWT: Tabular Feature Weighting with Transformer
by: Zhang, Xinhao, et al.
Published: (2024)
by: Zhang, Xinhao, et al.
Published: (2024)
Learning Relational Tabular Data without Shared Features
by: Wu, Zhaomin, et al.
Published: (2025)
by: Wu, Zhaomin, et al.
Published: (2025)
Deep Learning for School Dropout Detection: A Comparison of Tabular and Graph-Based Models for Predicting At-Risk Students
by: Almeida, Pablo G., et al.
Published: (2025)
by: Almeida, Pablo G., et al.
Published: (2025)
From Large Language Models and Optimization to Decision Optimization CoPilot: A Research Manifesto
by: Wasserkrug, Segev, et al.
Published: (2024)
by: Wasserkrug, Segev, et al.
Published: (2024)
Deep Tabular Representation Corrector
by: Ye, Hangting, et al.
Published: (2026)
by: Ye, Hangting, et al.
Published: (2026)
Not All Features Deserve Attention: Graph-Guided Dependency Learning for Tabular Data Generation with Language Models
by: Zhang, Zheyu, et al.
Published: (2025)
by: Zhang, Zheyu, et al.
Published: (2025)
Deep Learning on Graphs for Mobile Network Topology Generation
by: Meli, Felix Nannesson, et al.
Published: (2025)
by: Meli, Felix Nannesson, et al.
Published: (2025)
LLM Embeddings for Deep Learning on Tabular Data
by: Koloski, Boshko, et al.
Published: (2025)
by: Koloski, Boshko, et al.
Published: (2025)
Similar Items
-
Scalable Bayesian Structure Learning for Gaussian Graphical Models Using Marginal Pseudo-likelihood
by: Mohammadi, Reza, et al.
Published: (2023) -
Bayesian Structure Learning in Undirected Gaussian Graphical Models: Literature Review with Empirical Comparison
by: Vogels, Lucas, et al.
Published: (2023) -
Multiple Jump MCMC: A Scalable Algorithm for Bayesian Inference on Binary Model Spaces
by: Vogels, Lucas, et al.
Published: (2026) -
Modeling Alzheimer's Disease: Bayesian Copula Graphical Model from Demographic, Cognitive, and Neuroimaging Data
by: Vogels, Lucas, et al.
Published: (2024) -
Learning An Interpretable Risk Scoring System for Maximizing Decision Net Benefit
by: Chi, Wenhao, et al.
Published: (2026)