Learning on tabular data

November 30, 2020 — June 21, 2021

approximation

Bayes

clustering

high d

linear algebra

networks

optimization

probabilistic algorithms

probability

sparser than thou

statistics

Figure 1

Learning for tabular data, i.e. the stuff you generally store in spreadsheets and relational databases.

Popular in many areas, notably recommender systems.

(Cheng et al. 2016): jrzaurin/pytorch-widedeep: A flexible package to combine tabular data with text and images using Wide and Deep models in Pytorch. pytorch-widedeep, deep learning for tabular data IV: Deep Learning vs LightGBM

Note that the author of that package advises using gradient boosting machines to get this job done.

Neural Models for Tabular Data

1 References

Arik, and Pfister. 2019. “TabNet: Attentive Interpretable Tabular Learning.”

Cheng, Koc, Harmsen, et al. 2016. “Wide & Deep Learning for Recommender Systems.” arXiv:1606.07792 [Cs, Stat].

Gorishniy, Rubachev, Khrulkov, et al. 2023. “Revisiting Deep Learning Models for Tabular Data.”

Grinsztajn, Oyallon, and Varoquaux. 2022. “Why Do Tree-Based Models Still Outperform Deep Learning on Tabular Data?”

Montanari, and Weiner. 2023. “Compressing Tabular Data via Latent Variable Estimation.”

Richetti, Diakogianis, Bender, et al. 2023. “A Methods Guideline for Deep Learning for Tabular Data in Agriculture with a Case Study to Forecast Cereal Yield.” Computers and Electronics in Agriculture.

Shwartz-Ziv, and Armon. 2021. “Tabular Data: Deep Learning Is Not All You Need.” arXiv:2106.03253 [Cs].