1 citations · 2 across the 10 of their papers we have counts for
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Mapping and Measuring the Behavioral Evolution of Large Language Models
Dong Qiao, Chris Ding, Jicong Fan
Benchmark leaderboards summarize how well a language model performs, but not how its behavior relates to that of other models or changes across generations. We characterize the out…
CLUBench: A Clustering Benchmark
Feng Xiao, Dazhi Fu, Chris Ding +1
Clustering is a fundamental problem in data science with a long-standing research history, yielding numerous insightful algorithms. Despite this progress, a systematic and large-sc…
Explainable Graph Representation Learning via Graph Pattern Analysis
Xudong Wang, Ziheng Sun, Chris Ding +1
Explainable artificial intelligence (XAI) is an important area in the AI community, and interpretability is crucial for building robust and trustworthy AI models. While previous wo…
Energy Injection Identification enabled Disaggregation with Deep Multi-Task Learning
Xudong Wang, Guoming Tang, Junyu Xue +3
Non-Intrusive Load Monitoring (NILM) offers a cost-effective method to obtain fine-grained appliance-level energy consumption in smart homes and building applications. However, the…
GraphProp: Training the Graph Foundation Models using Graph Properties
Ziheng Sun, Qi Feng, Lehao Lin +2
This work focuses on training graph foundation models (GFMs) that have strong generalization ability in graph-level tasks such as graph classification. Effective GFM training requi…
Adaptive Riemannian Graph Neural Networks
Xudong Wang, Chris Ding, Tongxin Li +1
Graph data often exhibits complex geometric heterogeneity, where structures with varying local curvature, such as tree-like hierarchies and dense communities, coexist within a sing…
Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection
Xudong Wang, Ziheng Sun, Chris Ding +1
This work proposes a framework LGKDE that learns kernel density estimation for graphs. The key challenge in graph density estimation lies in effectively capturing both structural p…
K-means Derived Unsupervised Feature Selection using Improved ADMM
Ziheng Sun, Chris Ding, Jicong Fan
Feature selection is important for high-dimensional data analysis and is non-trivial in unsupervised learning problems such as dimensionality reduction and clustering. The goal of…