Imagen de Portada

ChainzRule: Sample-Efficient, Robust Deep Learning Across Tabular, NLP, and Vision Tasks

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autor principal:	Martnishn, Rowan
Formato:	Preprint
Publicado:	2026
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Layer-wise Derivative Controlled Networks
por: Martnishn, Rowan, et al.
Publicado: (2026)

On the Efficiency of NLP-Inspired Methods for Tabular Deep Learning
por: Thielmann, Anton Frederik, et al.
Publicado: (2024)

Layer-Wise Derivative Controlled Neural Networks Repo
por: Martnishn, Rowan
Publicado: (2026)

Sample-Efficient Tabular Self-Play for Offline Robust Reinforcement Learning
por: Li, Na, et al.
Publicado: (2025)

TabularBench: Benchmarking Adversarial Robustness for Tabular Deep Learning in Real-world Use-cases
por: Simonetto, Thibault, et al.
Publicado: (2024)

Towards Universal Tabular Embeddings: A Benchmark Across Data Tasks
por: Vogel, Liane, et al.
Publicado: (2026)

A Comprehensive Benchmark of Machine and Deep Learning Across Diverse Tabular Datasets
por: Shmuel, Assaf, et al.
Publicado: (2024)

Model Predictive Task Sampling for Efficient and Robust Adaptation
por: Wang, Qi, et al.
Publicado: (2025)

Improving Deep Tabular Learning
por: Sarafian, Sivan, et al.
Publicado: (2025)

TabM: Advancing Tabular Deep Learning with Parameter-Efficient Ensembling
por: Gorishniy, Yury, et al.
Publicado: (2024)

Automated Machine Learning for Unsupervised Tabular Tasks
por: Singh, Prabhant, et al.
Publicado: (2025)

Tabular Foundation Models Can Learn Association Rules
por: Karabulut, Erkan, et al.
Publicado: (2026)

Finding geodesics with the Deep Ritz method
por: Rowan, Conor
Publicado: (2025)

Distributionally Robust Multi-Task Reinforcement Learning via Adaptive Task Sampling
por: Corrado, Nicholas E., et al.
Publicado: (2026)

Benchmarking Optimizers for MLPs in Tabular Deep Learning
por: Gorishniy, Yury, et al.
Publicado: (2026)

NLP Sampling: Combining MCMC and NLP Methods for Diverse Constrained Sampling
por: Toussaint, Marc, et al.
Publicado: (2024)

SEMFED: Semantic-Aware Resource-Efficient Federated Learning for Heterogeneous NLP Tasks
por: Hussain, Sajid, et al.
Publicado: (2025)

Scalable Rule Lists Learning with Sampling
por: Pellegrina, Leonardo, et al.
Publicado: (2024)

ICLAD: In-Context Learning for Unified Tabular Anomaly Detection Across Supervision Regimes
por: Wei, Jack Yi, et al.
Publicado: (2026)

TILBench: A Systematic Benchmark for Tabular Imbalanced Learning Across Data Regimes
por: Liu, Ruizhe, et al.
Publicado: (2026)

A Survey on Deep Tabular Learning
por: Somvanshi, Shriyank, et al.
Publicado: (2024)

Tabular and Deep Learning for the Whittle Index
por: Relaño, Francisco Robledo, et al.
Publicado: (2024)

Multilingual Prompt Engineering in Large Language Models: A Survey Across NLP Tasks
por: Vatsal, Shubham, et al.
Publicado: (2025)

Tabular and Deep Reinforcement Learning for Gittins Index
por: Dhankhar, Harshit, et al.
Publicado: (2024)

Mambular: A Sequential Model for Tabular Deep Learning
por: Thielmann, Anton Frederik, et al.
Publicado: (2024)

Iterative Feature Exclusion Ranking for Deep Tabular Learning
por: Shaninah, Fathi Said Emhemed, et al.
Publicado: (2024)

Unveiling the Role of Data Uncertainty in Tabular Deep Learning
por: Kartashev, Nikolay, et al.
Publicado: (2025)

Deep Tabular Representation Corrector
por: Ye, Hangting, et al.
Publicado: (2026)

LLM Embeddings for Deep Learning on Tabular Data
por: Koloski, Boshko, et al.
Publicado: (2025)

The Role of Feature Interactions in Graph-based Tabular Deep Learning
por: Dubbeldam, Elias, et al.
Publicado: (2025)

A Closer Look at Deep Learning Methods on Tabular Datasets
por: Ye, Han-Jia, et al.
Publicado: (2024)

HOP to the Next Tasks and Domains for Continual Learning in NLP
por: Michieli, Umberto, et al.
Publicado: (2024)

Basis Transformers for Multi-Task Tabular Regression
por: Loh, Wei Min, et al.
Publicado: (2025)

Vision-based Deep Learning Analysis of Unordered Biomedical Tabular Datasets via Optimal Spatial Cartography
por: Mostafa, Sakib, et al.
Publicado: (2026)

LLM Empowered Prototype Learning for Zero and Few-Shot Tasks on Tabular Data
por: Wang, Peng, et al.
Publicado: (2025)

FlagGAM: Rule-Based Generalized Additive Modeling for Explainable Tabular Prediction
por: Zhao, Zijie, et al.
Publicado: (2026)

Provably Sample-Efficient Robust Reinforcement Learning with Average Reward
por: Roch, Zachary, et al.
Publicado: (2025)

MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts
por: Lee, Jihye, et al.
Publicado: (2025)

Discovering Association Rules in High-Dimensional Small Tabular Data
por: Karabulut, Erkan, et al.
Publicado: (2025)

Interpretable Deep Clustering for Tabular Data
por: Svirsky, Jonathan, et al.
Publicado: (2023)