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The analysis of tabular data has traditionally been dominated by gradient-boosted decision trees (GBDTs), known for their proficiency with mixed categorical and numerical features.
Xgboostlss–an extension of xgboost to probabilistic forecasting
März, A. (2019) · 1907
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Neural oblivious decision ensembles for deep learning on tabular data
Popov, S., Morozov, S., and Babenko, A. (2019) · 1909
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State-space models
Hamilton, J. D. (1994) · 1994
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Strictly proper scoring rules, prediction, and estimation
Gneiting, T. and Raftery, A. E. (2007) · 2007
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Generalized additive models for location scale and shape (gamlss) in r
Stasinopoulos, D. M. and Rigby, R. A. (2008) · 2008
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The elements of statistical learning: data mining, inference, and prediction
Hastie, T., Tibshirani, R., Friedman, J. H., and Friedman, J. H. (2009) · 2009
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Tabtransformer: Tabular data modeling using contextual embeddings
Huang, X., Khetan, A., Cvitkovic, M., and Karnin, Z. (2020) · 2012
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Overview of friedman’s test and post-hoc analysis
Pereira, D. G., Afonso, A., and Medeiros, F. M. (2015) · 2015
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Lightgbm: A highly efficient gradient boosting decision tree
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y. (2017) · 2017
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Catboost: unbiased boosting with categorical features
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., and Gulin, A. (2018) · 2018
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Tabnet: Attentive interpretable tabular learning
Arik, S. Ö. and Pfister, T. (2021) · 2021
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Revisiting deep learning models for tabular data
Gorishniy, Y., Rubachev, I., Khrulkov, V., and Babenko, A. (2021) · 2021
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Efficiently modeling long sequences with structured state spaces
Gu, A., Goel, K., and Ré, C. (2021) · 2021
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Deep neural networks and tabular data: A survey
Borisov, V., Leemann, T., Seßler, K., Haug, J., Pawelczyk, M., and Kasneci, G. (2022) · 2022
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On embeddings for numerical features in tabular deep learning
Gorishniy, Y., Rubachev, I., and Babenko, A. (2022) · 2022
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Why do tree-based models still outperform deep learning on typical tabular data?
Grinsztajn, L., Oyallon, E., and Varoquaux, G. (2022) · 2022
Cited alongside, same era.
Tabpfn: A transformer that solves small tabular classification problems in a second
Hollmann, N., Müller, S., Eggensperger, K., and Hutter, F. (2022) · 2022
Cited alongside, same era.
Distributional gradient boosting machines
März, A. and Kneib, T. (2022) · 2022
Cited alongside, same era.
Transtab: Learning transferable tabular transformers across tables
Jamba: A hybrid transformer-mamba language model
Lieber, O., Lenz, B., Bata, H., Cohen, G., Osin, J., Dalmedigos, I., Safahi, E., Meirom, S., Belinkov, Y., Shalev-Shwartz, S., et al. (2024) · 2024
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Liu, J., Yu, R., Wang, Y., Zheng, Y., Deng, T., Ye, W., and Wang, H. (2024) · 2024
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When do neural nets outperform boosted trees on tabular data?
McElfresh, D., Khandagale, S., Valverde, J., Prasad C, V., Ramakrishnan, G., Goldblum, M., and White, C. (2024) · 2024
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Simba: Simplified mamba-based architecture for vision and multivariate time series
Patro, B. N. and Agneeswaran, V. S. (2024) · 2024
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Moe-mamba: Efficient selective state space models with mixture of experts
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Wang, Z. and Sun, J. (2022) · 2022
Cited alongside, same era.
OpenML-CTR23 – a curated tabular regression benchmarking suite
Fischer, S. F., Feurer, L. H. M., and Bischl, B. (2023) · 2023
Cited alongside, same era.
Mamba: Linear-time sequence modeling with selective state spaces
Gu, A. and Dao, T. (2023) · 2023
Cited alongside, same era.
Rage against the mean–a review of distributional regression approaches
Kneib, T., Silbersdorff, A., and Säfken, B. (2023) · 2023
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Graph mamba: Towards learning on graphs with state space models
Behrouz, A. and Hashemi, F. (2024) · 2024
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Correia, A. and Alexandre, L. A. (2024) · 2024
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Bi-mamba4ts: Bidirectional mamba for time series forecasting
Liang, A., Jiang, X., Sun, Y., and Lu, C. (2024) · 2024
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Pióro, M., Ciebiera, K., Król, K., Ludziejewski, J., and Jaszczur, S. (2024) · 2024
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Caduceus: Bi-directional equivariant long-range dna sequence modeling
Schiff, Y., Kao, C.-H., Gokaslan, A., Dao, T., Gu, A., and Kuleshov, V. (2024) · 2024
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Is mamba effective for time series forecasting?
Wang, Z., Kong, F., Feng, S., Wang, M., Zhao, H., Wang, D., and Zhang, Y. (2024) · 2024
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A survey on vision mamba: Models, applications and challenges
Xu, R., Yang, S., Wang, Y., Du, B., and Chen, H. (2024) · 2024
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Vivim: a video vision mamba for medical video object segmentation
Yang, Y., Xing, Z., and Zhu, L. (2024) · 2024
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Medmamba: Vision mamba for medical image classification
Yue, Y. and Li, Z. (2024) · 2024
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Point could mamba: Point cloud learning via state space model
Zhang, T., Li, X., Yuan, H., Ji, S., and Yan, S. (2024) · 2024
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Cobra: Extending mamba to multi-modal large language model for efficient inference
Zhao, H., Zhang, M., Zhao, W., Ding, P., Huang, S., and Wang, D. (2024) · 2024
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