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.

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].
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].