19 citations · 21 across the 9 of their papers we have counts for
10 papers
Parameter-Free Encoders Remain Viable for RDB Foundation Models
Linjie Xu, David Wipf
Given a relational database (RDB) storing heterogeneous tabular information, how can we predict missing (or future) values in some target column of interest? As the space of potent…
RDBLearn: Simple In-Context Prediction Over Relational Databases
Yanlin Zhang, Linjie Xu, Quan Gan +2
Recent advances in tabular in-context learning (ICL) show that a single pretrained model can adapt to new prediction tasks from a small set of labeled examples, avoiding per-task t…
No Need to Train Your RDB Foundation Model
Linjie Xu, Yanlin Zhang, Quan Gan +2
Relational databases (RDBs) contain vast amounts of heterogeneous tabular information that can be exploited for predictive modeling purposes. But since the space of potential targe…
TaTToo: Tool-Grounded Thinking PRM for Test-Time Scaling in Tabular Reasoning
Jiaru Zou, Soumya Roy, Vinay Kumar Verma +6
Process Reward Models (PRMs) have recently emerged as a powerful framework for enhancing the reasoning capabilities of large reasoning models (LRMs), particularly in the context of…
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…
Griffin: Towards a Graph-Centric Relational Database Foundation Model
Yanbo Wang, Xiyuan Wang, Quan Gan +4
We introduce Griffin, the first foundation model attemptation designed specifically for Relational Databases (RDBs). Unlike previous smaller models focused on single RDB tasks, Gri…
Prior-Fitted Networks Scale to Larger Datasets When Treated as Weak Learners
Yuxin Wang, Botian Jiang, Yiran Guo +4
Prior-Fitted Networks (PFNs) have recently been proposed to efficiently perform tabular classification tasks. Although they achieve good performance on small datasets, they encount…
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 (…
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…
Efficient Link Prediction via GNN Layers Induced by Negative Sampling
Yuxin Wang, Xiannian Hu, Quan Gan +3
Graph neural networks (GNNs) for link prediction can loosely be divided into two broad categories. First, \emph{node-wise} architectures pre-compute individual embeddings for each…