Matthew Yang — accepted AI-conference papers

2025

CTSyn: A Foundation Model for Cross Tabular Data Generation

Xiaofeng Lin, Chenheng Xu, Matthew Yang, Guang Cheng

ICLR 2025poster

Generative Foundation Models (GFMs) have achieved remarkable success in producing high-quality synthetic data for images and text. However, their application to tabular data presents significant challenges due to the heterogeneous nature of table features. Current cross-table learning frameworks str…

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2023

“Low-Resource” Text Classification: A Parameter-Free Classification Method with Compressors

Zhiying Jiang, Matthew Yang, Mikhail Tsirlin, Raphael Tang, Yiqin Dai, Jimmy Lin

ACL 2023findings

Deep neural networks (DNNs) are often used for text classification due to their high accuracy. However, DNNs can be computationally intensive, requiring millions of parameters and large amounts of labeled data, which can make them expensive to use, to optimize, and to transfer to out-of-distribution…

Cited by 94 Source ↗PDF ↗code ↗Scholar ↗