23 citations · 35 across the 17 of their papers we have counts for
17 papers
Feedback Control for Multi-Objective Graph Self-Supervision
Karish Grover, Theodore Vasiloudis, Han Xie +3
Can multi-task self-supervised learning on graphs be coordinated without the usual tug-of-war between objectives? Graph self-supervised learning (SSL) offers a growing toolbox of p…
LAKEGEN: A LLM-based Tabular Corpus Generator for Evaluating Dataset Discovery in Data Lakes
Zhenwei Dai, Chuan Lei, Asterios Katsifodimos +3
How to generate a large, realistic set of tables along with joinability relationships, to stress-test dataset discovery methods? Dataset discovery methods aim to automatically iden…
Spectro-Riemannian Graph Neural Networks
Karish Grover, Haiyang Yu, Xiang Song +4
Can integrating spectral and curvature signals unlock new potential in graph representation learning? Non-Euclidean geometries, particularly Riemannian manifolds such as hyperbolic…
HybGRAG: Hybrid Retrieval-Augmented Generation on Textual and Relational Knowledge Bases
Meng-Chieh Lee, Qi Zhu, Costas Mavromatis +5
Given a semi-structured knowledge base (SKB), where text documents are interconnected by relations, how can we effectively retrieve relevant information to answer user questions? R…
Hierarchical Compression of Text-Rich Graphs via Large Language Models
Shichang Zhang, Da Zheng, Jiani Zhang +6
Text-rich graphs, prevalent in data mining contexts like e-commerce and academic graphs, consist of nodes with textual features linked by various relations. Traditional graph machi…
FeatNavigator: Automatic Feature Augmentation on Tabular Data
Jiaming Liang, Chuan Lei, Xiao Qin +4
Data-centric AI focuses on understanding and utilizing high-quality, relevant data in training machine learning (ML) models, thereby increasing the likelihood of producing accurate…
GraphStorm: all-in-one graph machine learning framework for industry applications
Da Zheng, Xiang Song, Qi Zhu +13
Graph machine learning (GML) is effective in many business applications. However, making GML easy to use and applicable to industry applications with massive datasets remain challe…
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…
GLEMOS: Benchmark for Instantaneous Graph Learning Model Selection
Namyong Park, Ryan Rossi, Xing Wang +3
The choice of a graph learning (GL) model (i.e., a GL algorithm and its hyperparameter settings) has a significant impact on the performance of downstream tasks. However, selecting…
McCatch: Scalable Microcluster Detection in Dimensional and Nondimensional Datasets
Braulio V. Sánchez Vinces, Robson L. F. Cordeiro, Christos Faloutsos
How could we have an outlier detector that works even with nondimensional data, and ranks together both singleton microclusters ('one-off' outliers) and nonsingleton microclusters…
OmniMatch: Effective Self-Supervised Any-Join Discovery in Tabular Data Repositories
Christos Koutras, Jiani Zhang, Xiao Qin +5
How can we discover join relationships among columns of tabular data in a data repository? Can this be done effectively when metadata is missing? Traditional column matching works…
Automatic Question-Answer Generation for Long-Tail Knowledge
Rohan Kumar, Youngmin Kim, Sunitha Ravi +4
Pretrained Large Language Models (LLMs) have gained significant attention for addressing open-domain Question Answering (QA). While they exhibit high accuracy in answering question…
EBV: Electronic Bee-Veterinarian for Principled Mining and Forecasting of Honeybee Time Series
Mst. Shamima Hossain, Christos Faloutsos, Boris Baer +2
Honeybees are vital for pollination and food production. Among many factors, extreme temperature (e.g., due to climate change) is particularly dangerous for bee health. Anticipatin…
Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Survey
Xi Fang, Weijie Xu, Fiona Anting Tan +7
Recent breakthroughs in large language modeling have facilitated rigorous exploration of their application in diverse tasks related to tabular data modeling, such as prediction, ta…
OpenTab: Advancing Large Language Models as Open-domain Table Reasoners
Kezhi Kong, Jiani Zhang, Zhengyuan Shen +5
Large Language Models (LLMs) trained on large volumes of data excel at various natural language tasks, but they cannot handle tasks requiring knowledge that has not been trained on…
Which Examples to Annotate for In-Context Learning? Towards Effective and Efficient Selection
Costas Mavromatis, Balasubramaniam Srinivasan, Zhengyuan Shen +4
Large Language Models (LLMs) can adapt to new tasks via in-context learning (ICL). ICL is efficient as it does not require any parameter updates to the trained LLM, but only few an…
ColdGuess: A General and Effective Relational Graph Convolutional Network to Tackle Cold Start Cases
Bo He, Xiang Song, Vincent Gao +1
Low-quality listings and bad actor behavior in online retail websites threatens e-commerce business as these result in sub-optimal buying experience and erode customer trust. When…