Jun-Peng Jiang @ LAMDA, NJU-AI

蒋俊鹏
Jun-Peng Jiang
Ph.D. candidate
LAMDA Group
School of Artificial Intelligence
Nanjing University, Nanjing 210023, China

Email: jiangjp@lamda.nju.edu.cn

• Jun-Peng Jiang, Si-Yang Liu, Hao-Run Cai, Qile Zhou, Han-Jia Ye.Representation Learning for Tabular Data: A Comprehensive Survey [Paper] [Code]

Tabular data, structured as rows and columns, is among the most prevalent data types in machine learning classification and regression applications. Models for learning from tabular data have continuously evolved, with Deep Neural Networks (DNNs) recently demonstrating promising results through their capability of representation learning. In this survey, we systematically introduce the field of tabular representation learning, covering the background, challenges, and benchmarks, along with the pros and cons of using DNNs. We organize existing methods into three main categories according to their generalization capabilities: specialized, transferable, and general models.

• Jun-Peng Jiang, Han-Jia Ye, Leye Wang, Yang Yang, Yuan Jiang, De-Chuan Zhan. Tabular Insights, Visual Impacts: Transferring Expertise from Tables to Images. In Proceedings of the 41st International Conference on Machine Learning (ICML-2024 Spotlight), Vienna, Austria, 2024. [Paper] [Code]

We propose CHannel tAbulaR alignment with optiMal tranSport(CHARMS), which establishes an alignment between image channels and tabular attributes, enabling selective knowledge transfer that is pertinent to visual features. By maximizing the mutual information between image channels and tabular features, knowledge from both numerical and categorical tabular attributes are extracted. Experimental results demonstrate that CHARMS not only enhances the performance of image classifiers but also improves their interpretability by effectively utilizing tabular knowledge.