1 citations · 1 across the 4 of their papers we have counts for
5 papers
Synthesize, Retrieve, and Propagate: A Unified Predictive Modeling Framework for Relational Databases
Ning Li, Kounianhua Du, Han Zhang +4
Relational databases (RDBs) have become the industry standard for storing massive and heterogeneous data. However, despite the widespread use of RDBs across various fields, the inh…
ELF-Gym: Evaluating Large Language Models Generated Features for Tabular Prediction
Yanlin Zhang, Ning Li, Quan Gan +3
Crafting effective features is a crucial yet labor-intensive and domain-specific task within machine learning pipelines. Fortunately, recent advancements in Large Language Models (…
Agentic Information Retrieval
Weinan Zhang, Junwei Liao, Ning Li +2
Since the 1970s, information retrieval (IR) has long been defined as the process of acquiring relevant information items from a pre-defined corpus to satisfy user information needs…
4DBInfer: A 4D Benchmarking Toolbox for Graph-Centric Predictive Modeling on Relational DBs
Minjie Wang, Quan Gan, David Wipf +17
Although RDBs store vast amounts of rich, informative data spread across interconnected tables, the progress of predictive machine learning models as applied to such tasks arguably…
GFS: Graph-based Feature Synthesis for Prediction over Relational Databases
Han Zhang, Quan Gan, David Wipf +1
Relational databases are extensively utilized in a variety of modern information system applications, and they always carry valuable data patterns. There are a huge number of data…