The field of tabular data synthesis and representation learning is advancing rapidly, with a focus on developing innovative methods for generating high-quality synthetic data while preserving individual privacy. Researchers are exploring new approaches to tabular data synthesis, including the use of large language models and diffusion-based models, which have shown promising results in terms of data quality and privacy preservation. Additionally, there is a growing interest in representation learning for tabular data, with a focus on developing deep neural networks that can learn effective representations of tabular data. Noteworthy papers in this area include one that proposes a benchmark for evaluating tabular data synthesis methods, highlighting the importance of fair and comprehensive comparisons among state-of-the-art methods. Another paper introduces the concept of surrogate public data, which can be used to replace traditional public data in differentially private machine learning. Furthermore, a survey on representation learning for tabular data provides a comprehensive overview of the field, covering the background, challenges, and benchmarks, as well as the pros and cons of using deep neural networks. A survey on synthetic tabular data generation also provides a unified and systematic review of existing methods, highlighting the importance of understanding the methodological interplay and open challenges in this area. Other notable research includes a one-stage end-to-end table structure recognition method, which achieves state-of-the-art performance on benchmark datasets, and a no-imputation incremental learning method for tabular data classification, which eliminates the need for imputation of missing values.