6 papers
Generative Auto-Bidding with Unified Modeling and Exploration
Mingming Zhang, Feiqing Zhuang, Na Li +7
Automated bidding is central to modern digital advertising. Early rule-based methods lacked adaptability, while subsequent Reinforcement Learning approaches modeled bidding as a Ma…
TabEmbed: Benchmarking and Learning Generalist Embeddings for Tabular Understanding
Minjie Qiang, Mingming Zhang, Xiaoyi Bao +5
Foundation models have established unified representations for natural language processing, yet this paradigm remains largely unexplored for tabular data. Existing methods face fun…
KMLP: A Scalable Hybrid Architecture for Web-Scale Tabular Data Modeling
Mingming Zhang, Pengfei Shi, Zhiqing Xiao +8
Predictive modeling on web-scale tabular data with billions of instances and hundreds of heterogeneous numerical features faces significant scalability challenges. These features e…
Q-Regularized Generative Auto-Bidding: From Suboptimal Trajectories to Optimal Policies
Mingming Zhang, Na Li, Zhuang Feiqing +8
With the rapid development of e-commerce, auto-bidding has become a key asset in optimizing advertising performance under diverse advertiser environments. The current approaches fo…
Beyond Tree Models: A Hybrid Model of KAN and gMLP for Large-Scale Financial Tabular Data
Mingming Zhang, Jiahao Hu, Pengfei Shi +8
Tabular data plays a critical role in real-world financial scenarios. Traditionally, tree models have dominated in handling tabular data. However, financial datasets in the industr…
AIGT: AI Generative Table Based on Prompt
Mingming Zhang, Zhiqing Xiao, Guoshan Lu +5
Tabular data, which accounts for over 80% of enterprise data assets, is vital in various fields. With growing concerns about privacy protection and data-sharing restrictions, gener…