Jicong Fan

GLASS: Graph-Language Alignment with Spherical Scoring for Transferable Graph-Level Anomaly Detection

Xudong Wang, Chris Ding, Tongxin Li +1

We introduce GLASS, a framework for graph-level anomaly detection (GLAD) that achieves robust cross-domain transferability through graph-language alignment on the unit hypersphere.…

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…

Many Voices, One Reward: Multi-Role Rubric Generation for LLM Judging and Reward Modeling

Dazhi Fu, Jiuding Yang, Yiwen Guo +1

Reliable reward and preference signals are critical for evaluating and optimizing large language models on open-ended tasks. Rubric-based judges offer a transparent way to decompos…

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…

Layer Embedding Deep Fusion Graph Neural Network

Taihua Xu, Genhao Tian, Jicong Fan +3

Graph Neural Networks (GNNs) have demonstrated impressive performance in learning representations from graph-structured data. However, their message-passing mechanism inherently re…

Sample Transform Cost-Based Training-Free Hallucination Detector for Large Language Models

Zeyang Ding, Xinglin Hu, Jicong Fan

Hallucinations in large language models (LLMs) remain a central obstacle to trustworthy deployment, motivating detectors that are accurate, lightweight, and broadly applicable. Sin…

GraphVec: Cross-Domain Graph Vectorization for Graph-Level Representation Learning

Qi Feng, Jicong Fan

Learning universal graph representations across heterogeneous domains is difficult because graph datasets differ in topology, node-attribute semantics, feature dimensions, and even…

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…

Medical Test-free Disease Detection Based on Big Data

Haokun Zhao, Yingzhe Bai, Qingyang Xu +3

Accurate disease detection is of paramount importance for effective medical treatment and patient care. However, the process of disease detection is often associated with extensive…

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…

UniOD: A Universal Model for Outlier Detection across Diverse Domains

Dazhi Fu, Jicong Fan

Outlier detection (OD), distinguishing inliers and outliers in completely unlabeled datasets, plays a vital role in science and engineering. Although there have been many insightfu…

Fairness-aware Anomaly Detection via Fair Projection

Feng Xiao, Xiaoying Tang, Jicong Fan

Unsupervised anomaly detection is a critical task in many high-social-impact applications such as finance, healthcare, social media, and cybersecurity, where demographics involving…

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…

Mutual Regression Distance

Dong Qiao, Jicong Fan

The maximum mean discrepancy and Wasserstein distance are popular distance measures between distributions and play important roles in many machine learning problems such as metric…

Non-Convex Tensor Recovery from Local Measurements

Tongle Wu, Ying Sun, Jicong Fan

Motivated by the settings where sensing the entire tensor is infeasible, this paper proposes a novel tensor compressed sensing model, where measurements are only obtained from sens…

cs.LG2024★ 1 cited

Federated t-SNE and UMAP for Distributed Data Visualization

Dong Qiao, Xinxian Ma, Jicong Fan

High-dimensional data visualization is crucial in the big data era and these techniques such as t-SNE and UMAP have been widely used in science and engineering. Big data, however,…

Multi-Subspace Matrix Recovery from Permuted Data

Liangqi Xie, Jicong Fan

This paper aims to recover a multi-subspace matrix from permuted data: given a matrix, in which the columns are drawn from a union of low-dimensional subspaces and some columns are…

Unsupervised Anomaly Detection for Tabular Data Using Noise Evaluation

Wei Dai, Kai Hwang, Jicong Fan

Unsupervised anomaly detection (UAD) plays an important role in modern data analytics and it is crucial to provide simple yet effective and guaranteed UAD algorithms for real appli…

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…

Graph Classification via Reference Distribution Learning: Theory and Practice

Zixiao Wang, Jicong Fan

Graph classification is a challenging problem owing to the difficulty in quantifying the similarity between graphs or representing graphs as vectors, though there have been a few m…