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Machine Learning on Tabular Data

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If you have a question about this talk, please contact Xianda Sun .

Data in tabular form is ubiquitous in industries such as finance, healthcare, and education. Historically, boosted decision trees and multi-layer perceptrons were the models of choice for making predictions on tabular data. In the last couple of years, neural process-based transformers (e.g. TabPFN, TabICL) that are trained on synthetic data have surpassed traditional approaches in terms of speed and accuracy. Well-funded start-ups have recently exploited these advances offering easy to use tools aimed at enterprises. In this talk we will 1) introduce the basic concepts behind tabular machine learning; 2) describe traditional tabular learning approaches including XGBoost, CatBoost, and RealMLP; 3) do deep dives on TabPFN and TabICL; 4) examine the use of LLMs for making tabular predictions; and finally 5) discuss some recent work on casual tabular foundation models.

This talk is part of the Machine Learning Reading Group @ CUED series.

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Seminar time is noon BST in Room FW 11, Willam Gates Hall. Zoom link: https://cl-cam-ac-uk.zoom.us/j/4361570789?pwd=Nkl2T3ZLaTZwRm05bzRTOUUxY3Q4QT09&from=addon in person & via zoom BP Institute

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