7 citations · 10 across the 6 of their papers we have counts for
6 papers
Make Still Further Progress: Chain of Thoughts for Tabular Data Leaderboard
Si-Yang Liu, Qile Zhou, Han-Jia Ye
Tabular data, a fundamental data format in machine learning, is predominantly utilized in competitions and real-world applications. The performance of tabular models--such as gradi…
Representation Learning for Tabular Data: A Comprehensive Survey
Jun-Peng Jiang, Si-Yang Liu, Hao-Run Cai +2
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 tabula…
TALENT: A Tabular Analytics and Learning Toolbox
Si-Yang Liu, Hao-Run Cai, Qi-Le Zhou +1
Tabular data is one of the most common data sources in machine learning. Although a wide range of classical methods demonstrate practical utilities in this field, deep learning met…
A Closer Look at Deep Learning Methods on Tabular Datasets
Han-Jia Ye, Si-Yang Liu, Hao-Run Cai +2
Tabular data is prevalent across diverse domains in machine learning. With the rapid progress of deep tabular prediction methods, especially pretrained (foundation) models, there i…
Rethinking Pre-Training in Tabular Data: A Neighborhood Embedding Perspective
Han-Jia Ye, Qi-Le Zhou, Huai-Hong Yin +2
Pre-training is prevalent in deep learning for vision and text data, leveraging knowledge from other datasets to enhance downstream tasks. However, for tabular data, the inherent h…
Unlocking the Transferability of Tokens in Deep Models for Tabular Data
Qi-Le Zhou, Han-Jia Ye, Le-Ye Wang +1
Fine-tuning a pre-trained deep neural network has become a successful paradigm in various machine learning tasks. However, such a paradigm becomes particularly challenging with tab…