Saved in:
| Main Authors: | Rubachev, Ivan, Kartashev, Nikolay, Gorishniy, Yury, Babenko, Artem |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.19380 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Unveiling the Role of Data Uncertainty in Tabular Deep Learning
by: Kartashev, Nikolay, et al.
Published: (2025)
by: Kartashev, Nikolay, et al.
Published: (2025)
Benchmarking Optimizers for MLPs in Tabular Deep Learning
by: Gorishniy, Yury, et al.
Published: (2026)
by: Gorishniy, Yury, et al.
Published: (2026)
On Finetuning Tabular Foundation Models
by: Rubachev, Ivan, et al.
Published: (2025)
by: Rubachev, Ivan, et al.
Published: (2025)
TabM: Advancing Tabular Deep Learning with Parameter-Efficient Ensembling
by: Gorishniy, Yury, et al.
Published: (2024)
by: Gorishniy, Yury, et al.
Published: (2024)
TabDDPM: Modelling Tabular Data with Diffusion Models
by: Kotelnikov, Akim, et al.
Published: (2022)
by: Kotelnikov, Akim, et al.
Published: (2022)
Talking Trees: Reasoning-Assisted Induction of Decision Trees for Tabular Data
by: Yakushev, George, et al.
Published: (2025)
by: Yakushev, George, et al.
Published: (2025)
Turning Tabular Foundation Models into Graph Foundation Models
by: Eremeev, Dmitry, et al.
Published: (2025)
by: Eremeev, Dmitry, et al.
Published: (2025)
TabPrep: Closing the Feature Engineering Gap in Tabular Benchmarks
by: Tschalzev, Andrej, et al.
Published: (2026)
by: Tschalzev, Andrej, et al.
Published: (2026)
ReTabAD: A Benchmark for Restoring Semantic Context in Tabular Anomaly Detection
by: Yoon, Sanghyu, et al.
Published: (2025)
by: Yoon, Sanghyu, et al.
Published: (2025)
ReTabSyn: Realistic Tabular Data Synthesis via Reinforcement Learning
by: Lin, Xiaofeng, et al.
Published: (2026)
by: Lin, Xiaofeng, et al.
Published: (2026)
TabArena: A Living Benchmark for Machine Learning on Tabular Data
by: Erickson, Nick, et al.
Published: (2025)
by: Erickson, Nick, et al.
Published: (2025)
TabFSBench: Tabular Benchmark for Feature Shifts in Open Environments
by: Cheng, Zi-Jian, et al.
Published: (2025)
by: Cheng, Zi-Jian, et al.
Published: (2025)
ReFill: Reinforcement Learning for Fill-In Minimization
by: Harb, Elfarouk, et al.
Published: (2025)
by: Harb, Elfarouk, et al.
Published: (2025)
TabPFGen -- Tabular Data Generation with TabPFN
by: Ma, Junwei, et al.
Published: (2024)
by: Ma, Junwei, et al.
Published: (2024)
TabAttackBench: A Benchmark for Adversarial Attacks on Tabular Data
by: He, Zhipeng, et al.
Published: (2025)
by: He, Zhipeng, et al.
Published: (2025)
LightAutoDS-Tab: Multi-AutoML Agentic System for Tabular Data
by: Lapin, Aleksey, et al.
Published: (2025)
by: Lapin, Aleksey, et al.
Published: (2025)
TabSeq: A Framework for Deep Learning on Tabular Data via Sequential Ordering
by: Habib, Al Zadid Sultan Bin, et al.
Published: (2024)
by: Habib, Al Zadid Sultan Bin, et al.
Published: (2024)
TabQL: In-Context Q-Learning with Tabular Foundation Models
by: Liu, Qisai, et al.
Published: (2026)
by: Liu, Qisai, et al.
Published: (2026)
TabFlex: Scaling Tabular Learning to Millions with Linear Attention
by: Zeng, Yuchen, et al.
Published: (2025)
by: Zeng, Yuchen, et al.
Published: (2025)
GraphPFN: A Prior-Data Fitted Graph Foundation Model
by: Eremeev, Dmitry, et al.
Published: (2025)
by: Eremeev, Dmitry, et al.
Published: (2025)
A critical look at the evaluation of GNNs under heterophily: Are we really making progress?
by: Platonov, Oleg, et al.
Published: (2023)
by: Platonov, Oleg, et al.
Published: (2023)
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)
Characterizing Graph Datasets for Node Classification: Homophily-Heterophily Dichotomy and Beyond
by: Platonov, Oleg, et al.
Published: (2022)
by: Platonov, Oleg, et al.
Published: (2022)
MambaTab: A Plug-and-Play Model for Learning Tabular Data
by: Ahamed, Md Atik, et al.
Published: (2024)
by: Ahamed, Md Atik, et al.
Published: (2024)
TabNSA: Native Sparse Attention for Efficient Tabular Data Learning
by: Eslamian, Ali, et al.
Published: (2025)
by: Eslamian, Ali, et al.
Published: (2025)
MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains
by: Lee, Kyungeun, et al.
Published: (2025)
by: Lee, Kyungeun, et al.
Published: (2025)
Response to Promises and Pitfalls of Deep Kernel Learning
by: Wilson, Andrew Gordon, et al.
Published: (2025)
by: Wilson, Andrew Gordon, et al.
Published: (2025)
SwitchTab: Switched Autoencoders Are Effective Tabular Learners
by: Wu, Jing, et al.
Published: (2024)
by: Wu, Jing, et al.
Published: (2024)
TabStruct: Measuring Structural Fidelity of Tabular Data
by: Jiang, Xiangjian, et al.
Published: (2025)
by: Jiang, Xiangjian, et al.
Published: (2025)
Attention versus Contrastive Learning of Tabular Data -- A Data-centric Benchmarking
by: Rabbani, Shourav B., et al.
Published: (2024)
by: Rabbani, Shourav B., et al.
Published: (2024)
TabSTAR: A Tabular Foundation Model for Tabular Data with Text Fields
by: Arazi, Alan, et al.
Published: (2025)
by: Arazi, Alan, et al.
Published: (2025)
MultiTab: A Scalable Foundation for Multitask Learning on Tabular Data
by: Sinodinos, Dimitrios, et al.
Published: (2025)
by: Sinodinos, Dimitrios, et al.
Published: (2025)
TabMixNN: A Unified Deep Learning Framework for Structural Mixed Effects Modeling on Tabular Data
by: Akdemir, Deniz
Published: (2025)
by: Akdemir, Deniz
Published: (2025)
Bridging the Gap between Sparse Matrix Reordering and Factorization: A Deep Learning Framework for Fill-in Reduction
by: Li, Ziwei, et al.
Published: (2026)
by: Li, Ziwei, et al.
Published: (2026)
Improving Deep Tabular Learning
by: Sarafian, Sivan, et al.
Published: (2025)
by: Sarafian, Sivan, et al.
Published: (2025)
Deep Clustering of Tabular Data by Weighted Gaussian Distribution Learning
by: Rabbani, Shourav B., et al.
Published: (2023)
by: Rabbani, Shourav B., et al.
Published: (2023)
Mind the Gap: A Framework for Assessing Pitfalls in Multimodal Active Learning
by: Eisenhardt, Dustin, et al.
Published: (2026)
by: Eisenhardt, Dustin, et al.
Published: (2026)
Federated Learning for Tabular Data using TabNet: A Vehicular Use-Case
by: Lindskog, William, et al.
Published: (2024)
by: Lindskog, William, et al.
Published: (2024)
TabImpute: Universal Zero-Shot Imputation for Tabular Data
by: Feitelberg, Jacob, et al.
Published: (2025)
by: Feitelberg, Jacob, et al.
Published: (2025)
Tab-PET: Graph-Based Positional Encodings for Tabular Transformers
by: Leng, Yunze, et al.
Published: (2025)
by: Leng, Yunze, et al.
Published: (2025)
Similar Items
-
Unveiling the Role of Data Uncertainty in Tabular Deep Learning
by: Kartashev, Nikolay, et al.
Published: (2025) -
Benchmarking Optimizers for MLPs in Tabular Deep Learning
by: Gorishniy, Yury, et al.
Published: (2026) -
On Finetuning Tabular Foundation Models
by: Rubachev, Ivan, et al.
Published: (2025) -
TabM: Advancing Tabular Deep Learning with Parameter-Efficient Ensembling
by: Gorishniy, Yury, et al.
Published: (2024) -
TabDDPM: Modelling Tabular Data with Diffusion Models
by: Kotelnikov, Akim, et al.
Published: (2022)