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Tabular data remains one of the most prevalent data types across a wide range of real-world applications, yet effective representation learning for this domain poses unique challenges due to its irregular patterns, heterogeneous feature distributions, and complex inter-column dependencies.
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Deepgbm: A deep learning framework distilled by gbdt for online prediction tasks
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Lumen segmentation of aortic dissection with cascaded convolutional network
Ziyan Li, Jianjiang Feng, Zishun Feng, Yunqiang An, Yang Gao, Bin Lu, and Jie Zhou · 2019
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Mtab: Matching tabular data to knowledge graph using probability models
Phuc Nguyen, Natthawut Kertkeidkachorn, Ryutaro Ichise, and Hideaki Takeda · 2019
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Neural oblivious decision ensembles for deep learning on tabular data
Sergei Popov, Stanislav Morozov, and Artem Babenko · 2019
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Gradient boosting neural networks: Grownet
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Generative adversarial networks
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Tabtransformer: Tabular data modeling using contextual embeddings
Xin Huang, Ashish Khetan, Milan Cvitkovic, and Zohar Karnin · 2020
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Net-dnf: Effective deep modeling of tabular data
Liran Katzir, Gal Elidan, and Ran El-Yaniv · 2020
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Network on network for tabular data classification in real-world applications
Yuanfei Luo, Hao Zhou, Wei-Wei Tu, Yuqiang Chen, Wenyuan Dai, and Qiang Yang · 2020
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Vaem: a deep generative model for heterogeneous mixed type data
Chao Ma, Sebastian Tschiatschek, Richard Turner, José Miguel Hernández-Lobato, and Cheng Zhang · 2020
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Handling incomplete heterogeneous data using vaes
Alfredo Nazabal, Pablo M Olmos, Zoubin Ghahramani, and Isabel Valera · 2020
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Optimization methods for interpretable differentiable decision trees applied to reinforcement learning
Andrew Silva, Matthew Gombolay, Taylor Killian, Ivan Jimenez, and Sung-Hyun Son · 2020
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Missing data imputation with adversarially-trained graph convolutional networks
Indro Spinelli, Simone Scardapane, and Aurelio Uncini · 2020
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Vime: Extending the success of self-and semi-supervised learning to tabular domain
Jinsung Yoon, Yao Zhang, James Jordon, and Mihaela van der Schaar · 2020
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Beyond homophily in graph neural networks: Current limitations and effective designs
Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra · 2020
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Tabnet: Attentive interpretable tabular learning
Sercan Ö Arik and Tomas Pfister · 2021
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Scarf: Self-supervised contrastive learning using random feature corruption
Dara Bahri, Heinrich Jiang, Yi Tay, and Donald Metzler · 2021
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Agebo-tabular: joint neural architecture and hyperparameter search with autotuned data-parallel training for tabular data
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A brief review of domain adaptation
Abolfazl Farahani, Sahar Voghoei, Khaled Rasheed, and Hamid R Arabnia · 2021
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Revisiting deep learning models for tabular data
Yury Gorishniy, Ivan Rubachev, Valentin Khrulkov, and Artem Babenko · 2021
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Explainable artificial intelligence for tabular data: A survey
Maria Sahakyan, Zeyar Aung, and Talal Rahwan · 2021
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Subtab: Subsetting features of tabular data for self-supervised representation learning
Talip Ucar, Ehsan Hajiramezanali, and Lindsay Edwards · 2021
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Ctab-gan: Effective table data synthesizing
Zilong Zhao, Aditya Kunar, Robert Birke, and Lydia Y Chen · 2021
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Deep neural networks and tabular data: A survey
Vadim Borisov, Tobias Leemann, Kathrin Seßler, Johannes Haug, Martin Pawelczyk, and Gjergji Kasneci · 2022
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Graphcare: Enhancing healthcare predictions with open-world personalized knowledge graphs
Pengcheng Jiang, Cao Xiao, Adam Cross, and Jimeng Sun · 2023
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Can differentiable decision trees learn interpretable reward functions?
Akansha Kalra and Daniel S Brown · 2023
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Tabddpm: Modelling tabular data with diffusion models
Akim Kotelnikov, Dmitry Baranchuk, Ivan Rubachev, and Artem Babenko · 2023
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Codi: Co-evolving contrastive diffusion models for mixed-type tabular synthesis
Chaejeong Lee, Jayoung Kim, and Noseong Park · 2023
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Tabgsl: Graph structure learning for tabular data prediction
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Vadim Borisov, Kathrin Seßler, Tobias Leemann, Martin Pawelczyk, and Gjergji Kasneci · 2022
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Tabcaps: A capsule neural network for tabular data classification with bow routing
Jintai Chen, KuanLun Liao, Yanwen Fang, Danny Chen, and Jian Wu · 2022
Cited alongside, same era.
Learning enhanced representation for tabular data via neighborhood propagation
Kounianhua Du, Weinan Zhang, Ruiwen Zhou, Yangkun Wang, Xilong Zhao, Jiarui Jin, Quan Gan, Zheng Zhang, and David P Wipf · 2022
Cited alongside, same era.
Tangos: Regularizing tabular neural networks through gradient orthogonalization and specialization
Alan Jeffares, Tennison Liu, Jonathan Crabbé, Fergus Imrie, and Mihaela van der Schaar · 2022
Cited alongside, same era.
Stasy: Score-based tabular data synthesis
Jayoung Kim, Chaejeong Lee, and Noseong Park · 2022
Cited alongside, same era.
Sos: Score-based oversampling for tabular data
Jayoung Kim, Chaejeong Lee, Yehjin Shin, Sewon Park, Minjung Kim, Noseong Park, and Jihoon Cho · 2022
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Transfer learning with deep tabular models
Roman Levin, Valeriia Cherepanova, Avi Schwarzschild, Arpit Bansal, C Bayan Bruss, Tom Goldstein, Andrew Gordon Wilson, and Micah Goldblum · 2022
Cited alongside, same era.
Jay Chiehen Liao and Cheng-Te Li · 2023
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From tabular data to knowledge graphs: A survey of semantic table interpretation tasks and methods
Jixiong Liu, Yoan Chabot, Raphaël Troncy, Viet-Phi Huynh, Thomas Labbé, and Pierre Monnin · 2023
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Learning optimal group-structured individualized treatment rules with many treatments
Haixu Ma, Donglin Zeng, and Yufeng Liu · 2023
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Tabret: Pre-training transformer-based tabular models for unseen columns
Soma Onishi, Kenta Oono, and Kohei Hayashi · 2023
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Wei Qin, Zetong Chen, Lei Wang, Yunshi Lan, Weijieying Ren, and Richang Hong · 2023
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High dimensional, tabular deep learning with an auxiliary knowledge graph
Camilo Ruiz, Hongyu Ren, Kexin Huang, and Jure Leskovec · 2023
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Findiff: Diffusion models for financial tabular data generation
Timur Sattarov, Marco Schreyer, and Damian Borth · 2023
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Natural language specification of reinforcement learning policies through differentiable decision trees
Pradyumna Tambwekar, Andrew Silva, Nakul Gopalan, and Matthew Gombolay · 2023
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Egg-gae: scalable graph neural networks for tabular data imputation
Lev Telyatnikov and Simone Scardapane · 2023
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Graphfade: Field-aware decorrelation neural network for graphs with tabular features
Junhong Wan, Yao Fu, Junlan Yu, Weihao Jiang, Shiliang Pu, and Ruiheng Yang · 2023
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Anypredict: Foundation model for tabular prediction
Zifeng Wang, Chufan Gao, Cao Xiao, and Jimeng Sun · 2023
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T2g-former: organizing tabular features into relation graphs promotes heterogeneous feature interaction
Jiahuan Yan, Jintai Chen, Yixuan Wu, Danny Z Chen, and Jian Wu · 2023
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Unitabe: Pretraining a unified tabular encoder for heterogeneous tabular data
Yazheng Yang, Yuqi Wang, Guang Liu, Ledell Wu, and Qi Liu · 2023
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Mixed-type tabular data synthesis with score-based diffusion in latent space
Hengrui Zhang, Jiani Zhang, Balasubramaniam Srinivasan, Zhengyuan Shen, Xiao Qin, Christos Faloutsos, Huzefa Rangwala, and George Karypis · 2023
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Skill disentanglement for imitation learning from suboptimal demonstrations
Tianxiang Zhao, Wenchao Yu, Suhang Wang, Lu Wang, Xiang Zhang, Yuncong Chen, Yanchi Liu, Wei Cheng, and Haifeng Chen · 2023
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Xtab: Cross-table pretraining for tabular transformers
Bingzhao Zhu, Xingjian Shi, Nick Erickson, Mu Li, George Karypis, and Mahsa Shoaran · 2023
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Inducing clusters deep kernel gaussian process for longitudinal data
Junjie Liang, Weijieying Ren, Hanifi Sahar, and Vasant Honavar · 2024
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Esacl: An efficient continual learning algorithm
Weijieying Ren and Vasant G Honavar · 2024
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Tablog: Test-time adaptation for tabular data using logic rules
Weijieying Ren, Xiaoting Li, Huiyuan Chen, Vineeth Rakesh, Zhuoyi Wang, Mahashweta Das, and Vasant G Honavar · 2024
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Language modeling on tabular data: A survey of foundations, techniques and evolution
Yucheng Ruan, Xiang Lan, Jingying Ma, Yizhi Dong, Kai He, and Mengling Feng · 2024
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A survey on self-supervised learning for non-sequential tabular data
Wei-Yao Wang, Wei-Wei Du, Derek Xu, Wei Wang, and Wen-Chih Peng · 2024
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Chain-of-table: Evolving tables in the reasoning chain for table understanding
Zilong Wang, Hao Zhang, Chun-Liang Li, Julian Martin Eisenschlos, Vincent Perot, Zifeng Wang, Lesly Miculicich, Yasuhisa Fujii, Jingbo Shang, Chen-Yu Lee, et al · 2024
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Ratt: Athought structure for coherent and correct llmreasoning
Jinghan Zhang, Xiting Wang, Weijieying Ren, Lu Jiang, Dongjie Wang, and Kunpeng Liu · 2024
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Interpretable imitation learning with dynamic causal relations
Tianxiang Zhao, Wenchao Yu, Suhang Wang, Lu Wang, Xiang Zhang, Yuncong Chen, Yanchi Liu, Wei Cheng, and Haifeng Chen · 2024
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