Cover Image

LoMETab: Beyond Rank-1 Ensembles for Tabular Deep Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Choi, Changryeol, Park, Hyewon, Kwon, Yujin, Jeong, Gowun
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

TimeBridge: Better Diffusion Prior Design with Bridge Models for Time Series Generation
by: Park, Jinseong, et al.
Published: (2024)

Rethinking the Rank Threshold for LoRA Fine-Tuning
by: Park, Juneyoung
Published: (2026)

FALQON: Accelerating LoRA Fine-tuning with Low-Bit Floating-Point Arithmetic
by: Choi, Kanghyun, et al.
Published: (2025)

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

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

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

Stability Analysis of Sharpness-Aware Minimization
by: Kim, Hoki, et al.
Published: (2023)

Adversarial Reinforcement Learning Framework for ESP Cheater Simulation
by: Park, Inkyu, et al.
Published: (2025)

Tabular Data: Is Deep Learning all you need?
by: Zabërgja, Guri, et al.
Published: (2024)

How Ensemble Learning Balances Accuracy and Overfitting: A Bias-Variance Perspective on Tabular Data
by: Mohammad, Zubair Ahmed
Published: (2025)

Beyond Factor Aggregation: Gauge-Aware Low-Rank Server Representations for Federated LoRA
by: Chen, Jinqian, et al.
Published: (2026)

Can TabPFN Compete with GNNs for Node Classification via Graph Tabularization?
by: Choi, Jeongwhan, et al.
Published: (2025)

Deep Learning with Tabular Data: A Self-supervised Approach
by: Vyas, Tirth Kiranbhai
Published: (2024)

Deep Clustering of Tabular Data by Weighted Gaussian Distribution Learning
by: Rabbani, Shourav B., et al.
Published: (2023)

Text-to-LoRA: Instant Transformer Adaption
by: Charakorn, Rujikorn, et al.
Published: (2025)

GraLoRA: Granular Low-Rank Adaptation for Parameter-Efficient Fine-Tuning
by: Jung, Yeonjoon, et al.
Published: (2025)

Rank-1 LoRAs Encode Interpretable Reasoning Signals
by: Ward, Jake, et al.
Published: (2025)

Each Rank Could be an Expert: Single-Ranked Mixture of Experts LoRA for Multi-Task Learning
by: Zhao, Ziyu, et al.
Published: (2025)

Deep Feature Embedding for Tabular Data
by: Wu, Yuqian, et al.
Published: (2024)

Boosting Relational Deep Learning with Pretrained Tabular Models
by: Lachi, Veronica, et al.
Published: (2025)

Stable-LoRA: Stabilizing Feature Learning of Low-Rank Adaptation
by: Wu, Yize, et al.
Published: (2026)

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

Graph Neural Network contextual embedding for Deep Learning on Tabular Data
by: Villaizán-Vallelado, Mario, et al.
Published: (2023)

Laplacian-LoRA: Delaying Oversmoothing in Deep GCNs via Spectral Low-Rank Adaptation
by: Alisetti, Sai Vamsi
Published: (2026)

Unsupervised Outlier Detection using Random Subspace and Subsampling Ensembles of Dirichlet Process Mixtures
by: Kim, Dongwook, et al.
Published: (2024)

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

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

ProtoNAM: Prototypical Neural Additive Models for Interpretable Deep Tabular Learning
by: Xiong, Guangzhi, et al.
Published: (2024)

RRADistill: Distilling LLMs' Passage Ranking Ability for Long-Tail Queries Document Re-Ranking on a Search Engine
by: Choi, Nayoung, et al.
Published: (2024)

AR1-ZO: Topology-Aware Rank-1 Zeroth-Order Queries for High-Rank LoRA Fine-Tuning
by: Chen, Ziye, et al.
Published: (2026)

Heuristic Algorithm-based Action Masking Reinforcement Learning (HAAM-RL) with Ensemble Inference Method
by: Choi, Kyuwon, et al.
Published: (2024)

Enriching Tabular Data with Contextual LLM Embeddings: A Comprehensive Ablation Study for Ensemble Classifiers
by: Kasneci, Gjergji, et al.
Published: (2024)

TabSeq: A Framework for Deep Learning on Tabular Data via Sequential Ordering
by: Habib, Al Zadid Sultan Bin, et al.
Published: (2024)

PANDA: Expanded Width-Aware Message Passing Beyond Rewiring
by: Choi, Jeongwhan, et al.
Published: (2024)

Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach
by: Mohan, Adithya, et al.
Published: (2025)

Personalized Product Search Ranking: A Multi-Task Learning Approach with Tabular and Non-Tabular Data
by: Morishetti, Lalitesh, et al.
Published: (2025)

DeLo: Dual Decomposed Low-Rank Experts Collaboration for Continual Missing Modality Learning
by: Liu, Xiwei, et al.
Published: (2026)

To Predict or Not To Predict? Proportionally Masked Autoencoders for Tabular Data Imputation
by: Kim, Jungkyu, et al.
Published: (2024)

The Disparate Benefits of Deep Ensembles
by: Schweighofer, Kajetan, et al.
Published: (2024)

TabularMath: Evaluating Computational Extrapolation in Tabular Learning via Program-Verified Synthesis
by: Cheng, Zerui, et al.
Published: (2026)