Team up GBDTs and DNNs: Advancing Efficient and Effective Tabular Prediction with Tree-hybrid MLPs
Fuente:
arXiv
Guardado en:
| Autores principales: | Yan, Jiahuan, Chen, Jintai, Wang, Qianxing, Chen, Danny Z., Wu, Jian |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
ExcelFormer: A neural network surpassing GBDTs on tabular data
por: Chen, Jintai, et al.
Publicado: (2023)
por: Chen, Jintai, et al.
Publicado: (2023)
Making Pre-trained Language Models Great on Tabular Prediction
por: Yan, Jiahuan, et al.
Publicado: (2024)
por: Yan, Jiahuan, et al.
Publicado: (2024)
Small Models are LLM Knowledge Triggers on Medical Tabular Prediction
por: Yan, Jiahuan, et al.
Publicado: (2024)
por: Yan, Jiahuan, et al.
Publicado: (2024)
Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding
por: Gao, Chufan, et al.
Publicado: (2025)
por: Gao, Chufan, et al.
Publicado: (2025)
Reinforcing Numerical Reasoning in LLMs for Tabular Prediction via Structural Priors
por: Cai, Pengxiang, et al.
Publicado: (2025)
por: Cai, Pengxiang, et al.
Publicado: (2025)
Benchmarking Optimizers for MLPs in Tabular Deep Learning
por: Gorishniy, Yury, et al.
Publicado: (2026)
por: Gorishniy, Yury, et al.
Publicado: (2026)
OmniTabBench: Mapping the Empirical Frontiers of GBDTs, Neural Networks, and Foundation Models for Tabular Data at Scale
por: Jiang, Dihong, et al.
Publicado: (2026)
por: Jiang, Dihong, et al.
Publicado: (2026)
Better by Default: Strong Pre-Tuned MLPs and Boosted Trees on Tabular Data
por: Holzmüller, David, et al.
Publicado: (2024)
por: Holzmüller, David, et al.
Publicado: (2024)
Cross-Table Pretraining towards a Universal Function Space for Heterogeneous Tabular Data
por: Chen, Jintai, et al.
Publicado: (2024)
por: Chen, Jintai, et al.
Publicado: (2024)
Triangulating PL functions and the existence of efficient ReLU DNNs
por: Calegari, Danny
Publicado: (2025)
por: Calegari, Danny
Publicado: (2025)
Personalized Heart Disease Detection via ECG Digital Twin Generation
por: Hu, Yaojun, et al.
Publicado: (2024)
por: Hu, Yaojun, et al.
Publicado: (2024)
Effective and Memory-Efficient Alternatives to ECC for Reliable Large-Scale DNNs
por: Ahmadilivani, Mohammad Hasan, et al.
Publicado: (2026)
por: Ahmadilivani, Mohammad Hasan, et al.
Publicado: (2026)
Pretrained deep models outperform GBDTs in Learning-To-Rank under label scarcity
por: Hou, Charlie, et al.
Publicado: (2023)
por: Hou, Charlie, et al.
Publicado: (2023)
Interpolated-MLPs: Controllable Inductive Bias
por: Wu, Sean, et al.
Publicado: (2024)
por: Wu, Sean, et al.
Publicado: (2024)
Constructing Efficient Fact-Storing MLPs for Transformers
por: Dugan, Owen, et al.
Publicado: (2025)
por: Dugan, Owen, et al.
Publicado: (2025)
SwitchTab: Switched Autoencoders Are Effective Tabular Learners
por: Wu, Jing, et al.
Publicado: (2024)
por: Wu, Jing, et al.
Publicado: (2024)
TrialEnroll: Predicting Clinical Trial Enrollment Success with Deep & Cross Network and Large Language Models
por: Yue, Ling, et al.
Publicado: (2024)
por: Yue, Ling, et al.
Publicado: (2024)
Learning to Model Graph Structural Information on MLPs via Graph Structure Self-Contrasting
por: Wu, Lirong, et al.
Publicado: (2024)
por: Wu, Lirong, et al.
Publicado: (2024)
Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference
por: Liu, Yunhui, et al.
Publicado: (2024)
por: Liu, Yunhui, et al.
Publicado: (2024)
Converting MLPs into Polynomials in Closed Form
por: Belrose, Nora, et al.
Publicado: (2025)
por: Belrose, Nora, et al.
Publicado: (2025)
Boosting MLPs with a Coarsening Strategy for Long-Term Time Series Forecasting
por: Bian, Nannan, et al.
Publicado: (2024)
por: Bian, Nannan, et al.
Publicado: (2024)
On Hardening DNNs against Noisy Computations
por: Wang, Xiao, et al.
Publicado: (2025)
por: Wang, Xiao, et al.
Publicado: (2025)
Understanding DNNs in Feature Interaction Models: A Dimensional Collapse Perspective
por: Wang, Jiancheng, et al.
Publicado: (2026)
por: Wang, Jiancheng, et al.
Publicado: (2026)
TabSieve: Explicit In-Table Evidence Selection for Tabular Prediction
por: Wang, Yongyao, et al.
Publicado: (2026)
por: Wang, Yongyao, et al.
Publicado: (2026)
Uncertainty Quantification on Clinical Trial Outcome Prediction
por: Chen, Tianyi, et al.
Publicado: (2024)
por: Chen, Tianyi, et al.
Publicado: (2024)
TabM: Advancing Tabular Deep Learning with Parameter-Efficient Ensembling
por: Gorishniy, Yury, et al.
Publicado: (2024)
por: Gorishniy, Yury, et al.
Publicado: (2024)
Heuristic Methods are Good Teachers to Distill MLPs for Graph Link Prediction
por: Qin, Zongyue, et al.
Publicado: (2025)
por: Qin, Zongyue, et al.
Publicado: (2025)
MLPs at the EOC: Spectrum of the NTK
por: Terjék, Dávid, et al.
Publicado: (2025)
por: Terjék, Dávid, et al.
Publicado: (2025)
MLPs at the EOC: Concentration of the NTK
por: Terjék, Dávid, et al.
Publicado: (2025)
por: Terjék, Dávid, et al.
Publicado: (2025)
A Survey on Ordinal Regression: Applications, Advances and Prospects
por: Wang, Jinhong, et al.
Publicado: (2025)
por: Wang, Jinhong, et al.
Publicado: (2025)
Bilinear MLPs enable weight-based mechanistic interpretability
por: Pearce, Michael T., et al.
Publicado: (2024)
por: Pearce, Michael T., et al.
Publicado: (2024)
Hyperparameter Tuning MLPs for Probabilistic Time Series Forecasting
por: Madhusudhanan, Kiran, et al.
Publicado: (2024)
por: Madhusudhanan, Kiran, et al.
Publicado: (2024)
Hessian-aware Training for Enhancing DNNs Resilience to Parameter Corruptions
por: Prato, Tahmid Hasan, et al.
Publicado: (2025)
por: Prato, Tahmid Hasan, et al.
Publicado: (2025)
Training MLPs on Graphs without Supervision
por: Wang, Zehong, et al.
Publicado: (2024)
por: Wang, Zehong, et al.
Publicado: (2024)
Theoretical Analysis of Robust Overfitting for Wide DNNs: An NTK Approach
por: Fu, Shaopeng, et al.
Publicado: (2023)
por: Fu, Shaopeng, et al.
Publicado: (2023)
Mimetic Initialization of MLPs
por: Trockman, Asher, et al.
Publicado: (2026)
por: Trockman, Asher, et al.
Publicado: (2026)
Tree-Regularized Tabular Embeddings
por: Li, Xuan, et al.
Publicado: (2024)
por: Li, Xuan, et al.
Publicado: (2024)
Edge-free but Structure-aware: Prototype-Guided Knowledge Distillation from GNNs to MLPs
por: Wu, Taiqiang, et al.
Publicado: (2023)
por: Wu, Taiqiang, et al.
Publicado: (2023)
Machine Unlearning for Robust DNNs: Attribution-Guided Partitioning and Neuron Pruning in Noisy Environments
por: Jin, Deliang, et al.
Publicado: (2025)
por: Jin, Deliang, et al.
Publicado: (2025)
Structural Disentanglement in Bilinear MLPs via Architectural Inductive Bias
por: Nema, Ojasva, et al.
Publicado: (2026)
por: Nema, Ojasva, et al.
Publicado: (2026)
Ejemplares similares
-
ExcelFormer: A neural network surpassing GBDTs on tabular data
por: Chen, Jintai, et al.
Publicado: (2023) -
Making Pre-trained Language Models Great on Tabular Prediction
por: Yan, Jiahuan, et al.
Publicado: (2024) -
Small Models are LLM Knowledge Triggers on Medical Tabular Prediction
por: Yan, Jiahuan, et al.
Publicado: (2024) -
Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding
por: Gao, Chufan, et al.
Publicado: (2025) -
Reinforcing Numerical Reasoning in LLMs for Tabular Prediction via Structural Priors
por: Cai, Pengxiang, et al.
Publicado: (2025)