Fetching the paper…
Reading the bibliography…
Leveraging the in-context learning (ICL) capability of Large Language Models (LLMs) for tabular classification has gained significant attention for its training-free adaptability across diverse datasets.
The regression analysis of binary sequences
Cox, D. R · 1958
Earlier work this paper cites.
Nearest neighbor pattern classification
Cover, T. and Hart, P · 1967
Earlier work this paper cites.
Induction of decision trees
Quinlan, J. R · 1986
Earlier work this paper cites.
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., and Williams, R. J · 1986
Earlier work this paper cites.
Principal components analysis (pca)
Maćkiewicz, A. and Ratajczak, W · 1993
Earlier work this paper cites.
Support-vector networks
Cortes, C · 1995
Earlier work this paper cites.
Greedy function approximation: a gradient boosting machine
Friedman, J. H · 2001
Earlier work this paper cites.
Classification and regression by randomforest
Liaw, A., Wiener, M., et al · 2002
Earlier work this paper cites.
The random projection method , volume 65
Vempala, S. S · 2005
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
Earlier work this paper cites.
MNIST handwritten digit database
LeCun, Y., Cortes, C., and Burges, C · 2010
Earlier work this paper cites.
OpenML: Networked science in machine learning
Vanschoren, J., van Rijn, J. N., Bischl, B., and Torgo, L · 2013
Earlier work this paper cites.
Loan approval prediction based on machine learning approach
Arun, K., Ishan, G., and Sanmeet, K · 2016
Earlier work this paper cites.
Xgboost: A scalable tree boosting system
Chen, T. and Guestrin, C · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
Mimic-iii, a freely accessible critical care database
Johnson, A. E., Pollard, T. J., Shen, L., Lehman, L.-w. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Anthony Celi, L., and Mark, R. G · 2016
Earlier work this paper cites.
Deepfm: a factorization-machine based neural network for ctr prediction
Guo, H., Tang, R., Ye, Y., Li, Z., and He, X · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Earlier work this paper cites.
Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms
Xiao, H., Rasul, K., and Vollgraf, R · 2017
Earlier work this paper cites.
Catboost: unbiased boosting with categorical features
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., and Gulin, A · 2018
Earlier work this paper cites.
Bischl, B., Casalicchio, G., Feurer, M., Hutter, F., Lang, M., Mantovani, R. G., van Rijn, J. N., and Vanschoren, J · 2019
Earlier work this paper cites.
Neural oblivious decision ensembles for deep learning on tabular data
Popov, S., Morozov, S., and Babenko, A · 2019
Earlier work this paper cites.
Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., and Fergus, R · 2019
Earlier work this paper cites.
Deep learning based recommender system: A survey and new perspectives
Zhang, S., Yao, L., Sun, A., and Tay, Y · 2019
Earlier work this paper cites.
LongFormer: The long-document transformer
Beltagy, I., Peters, M. E., and Cohan, A · 2020
Earlier work this paper cites.
Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
Cited alongside, same era.
Tabtransformer: Tabular data modeling using contextual embeddings
Huang, X., Khetan, A., Cvitkovic, M., and Karnin, Z · 2020
Cited alongside, same era.
Transformers are rnns: Fast autoregressive transformers with linear attention
Katharopoulos, A., Vyas, A., Pappas, N., and Fleuret, F · 2020
Cited alongside, same era.
Feature selection using stochastic gates
Yamada, Y., Lindenbaum, O., Negahban, S., and Kluger, Y · 2020
Cited alongside, same era.
Vime: Extending the success of self-and semi-supervised learning to tabular domain
Yoon, J., Zhang, Y., Jordon, J., and Van der Schaar, M · 2020
Cited alongside, same era.
Rwkv: Reinventing rnns for the transformer era
Peng, B., Alcaide, E., Anthony, Q. G., Albalak, A., Arcadinho, S., Biderman, S., Cao, H., Cheng, X., Chung, M. N., Derczynski, L., et al · 2023
Later among the works it cites.
Retentive network: A successor to transformer for large language models
Sun, Y., Dong, L., Huang, S., Ma, S., Xia, Y., Xue, J., Wang, J., and Wei, F · 2023
Later among the works it cites.
Eegformer: A transformer–based brain activity classification method using eeg signal
Wan, Z., Li, M., Liu, S., Huang, J., Tan, H., and Duan, W · 2023
Later among the works it cites.
XTab: Cross-table pretraining for tabular transformers
Zhu, B., Shi, X., Erickson, N., Li, M., Karypis, G., and Shoaran, M · 2023
Later among the works it cites.
Mambatab: A simple yet effective approach for handling tabular data
Ahamed, M. A. and Cheng, Q · 2024
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Agarwal, R., Melnick, L., Frosst, N., Zhang, X., Lengerich, B., Caruana, R., and Hinton, G. E · 2021
Cited alongside, same era.
Tabnet: Attentive interpretable tabular learning
Arik, S. Ö. and Pfister, T · 2021
Cited alongside, same era.
Auto-sklearn 2.0: Hands-free automl via meta-learning
Feurer, M., Eggensperger, K., Falkner, S., Lindauer, M., and Hutter, F · 2021
Cited alongside, same era.
Revisiting deep learning models for tabular data
Gorishniy, Y., Rubachev, I., Khrulkov, V., and Babenko, A · 2021
Cited alongside, same era.
Combining recurrent, convolutional, and continuous-time models with linear state space layers
Gu, A., Johnson, I., Goel, K., Saab, K. K., Dao, T., Rudra, A., and Re, C · 2021
Cited alongside, same era.
Saint: Improved neural networks for tabular data via row attention and contrastive pre-training
Somepalli, G., Goldblum, M., Schwarzschild, A., Bruss, C. B., and Goldstein, T · 2021
Cited alongside, same era.
Danets: Deep abstract networks for tabular data classification and regression
Chen, J., Liao, K., Wan, Y., Chen, D. Z., and Wu, J · 2022
Cited alongside, same era.
Hyperfast: Instant classification for tabular data
Bonet, D., Montserrat, D. M., Giró-i Nieto, X., and Ioannidis, A. G · 2024
Later among the works it cites.
Chu, Y., Xu, J., Yang, Q., Wei, H., Wei, X., Guo, Z., Leng, Y., Lv, Y., He, J., Lin, J., et al · 2024
Later among the works it cites.
Flashattention-2: Faster attention with better parallelism and work partitioning
Dao, T · 2024
Later among the works it cites.
Transformers are SSMs: Generalized models and efficient algorithms through structured state space duality
Dao, T. and Gu, A · 2024
Later among the works it cites.
CausalLM is not optimal for in-context learning
Ding, N., Levinboim, T., Wu, J., Goodman, S., and Soricut, R · 2024
Later among the works it cites.
Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Yang, A., Fan, A., et al · 2024
Later among the works it cites.
Tunetables: Context optimization for scalable prior-data fitted networks
Feuer, B., Schirrmeister, R. T., Cherepanova, V., Hegde, C., Hutter, F., Goldblum, M., Cohen, N., and White, C · 2024
Later among the works it cites.
TabR: Tabular deep learning meets nearest neighbors
Gorishniy, Y., Rubachev, I., Kartashev, N., Shlenskii, D., Kotelnikov, A., and Babenko, A · 2024
Later among the works it cites.
Mamba: Linear-time sequence modeling with selective state spaces
Gu, A. and Dao, T · 2024
Later among the works it cites.
Simulating 500 million years of evolution with a language model
Hayes, T., Rao, R., Akin, H., Sofroniew, N. J., Oktay, D., Lin, Z., Verkuil, R., Tran, V. Q., Deaton, J., Wiggert, M., Badkundri, R., Shafkat, I., Gong, J., Derry, A., Molina, R. S., Thomas, N., Khan, Y. A., Mishra, C., Kim, C., Bartie, L. J., Nemeth, M., Hsu, P. D., Sercu, T., Candido, S., and Rives, A · 2024
Later among the works it cites.
In-context data distillation with TabPFN
Ma, J., Thomas, V., Yu, G., and Caterini, A · 2024
Later among the works it cites.
Flashattention-3: Fast and accurate attention with asynchrony and low-precision
Shah, J., Bikshandi, G., Zhang, Y., Thakkar, V., Ramani, P., and Dao, T · 2024
Later among the works it cites.
Mambular: A sequential model for tabular deep learning
Thielmann, A. F., Kumar, M., Weisser, C., Reuter, A., Säfken, B., and Samiee, S · 2024
Later among the works it cites.
Retrieval & fine-tuning for in-context tabular models
Thomas, V., Ma, J., Hosseinzadeh, R., Golestaneh, K., Yu, G., Volkovs, M., and Caterini, A. L · 2024
Later among the works it cites.
BiSHop: Bi-directional cellular learning for tabular data with generalized sparse modern hopfield model
Xu, C., Huang, Y.-C., Hu, J. Y.-C., Li, W., Gilani, A., Goan, H.-S., and Liu, H · 2024
Later among the works it cites.
Gated linear attention transformers with hardware-efficient training
Yang, S., Wang, B., Shen, Y., Panda, R., and Kim, Y · 2024
Later among the works it cites.
When linear attention meets autoregressive decoding: Towards more effective and efficient linearized large language models
You, H., Fu, Y., Wang, Z., Yazdanbakhsh, A., and Lin, Y. C · 2024
Later among the works it cites.
The hedgehog & the porcupine: Expressive linear attentions with softmax mimicry
Zhang, M., Bhatia, K., Kumbong, H., and Re, C · 2024
Later among the works it cites.
Accurate predictions on small data with a tabular foundation model
Hollmann, N., Müller, S., Purucker, L., Krishnakumar, A., Körfer, M., Hoo, S. B., Schirrmeister, R. T., and Hutter, F · 2025
Closest in time.
Mixture of in-context prompters for tabular PFNs
Xu, D. Q., Cirik, F. O., Asadi, R., Sun, Y., and Wang, W · 2025
Closest in time.