56 citations · 201 across the 50 of their papers we have counts for
53 papers
Recall What Is Missing: Composing Evidence from Long-Term Agent Memory
Qi Feng, Jicong Fan
Under a limited context budget, a long-term agent must recall not merely memories that are individually relevant, but evidence that is collectively sufficient for the current query…
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.…
The Retriever Should Remember: Experience-Amortized Reranking for Long-Term Agent Memory
Qi Feng, Chris Ding, Jicong Fan
Long-term language-model agents accumulate memories across interactions, but their retrievers typically do not accumulate retrieval experience. Semantic retrieval is efficient, but…
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…
An Interdisciplinary and Cross-Task Review on Missing Data Imputation
Jicong Fan
Missing data is a fundamental challenge in data science, significantly hindering analysis and decision-making across a wide range of disciplines, including healthcare, bioinformati…
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…
Text-ADBench: Text Anomaly Detection Benchmark Based on LLM Embeddings
Feng Xiao, Jicong Fan
Text anomaly detection is a critical task in natural language processing (NLP), with applications spanning fraud detection, misinformation identification, spam detection and conten…
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…
Subject Information Extraction for Novelty Detection with Domain Shifts
Yangyang Qu, Dazhi Fu, Jicong Fan
Unsupervised novelty detection (UND), aimed at identifying novel samples, is essential in fields like medical diagnosis, cybersecurity, and industrial quality control. Most existin…
FCBoost-Net: A Generative Network for Synthesizing Multiple Collocated Outfits via Fashion Compatibility Boosting
Dongliang Zhou, Haijun Zhang, Jianghong Ma +2
Outfit generation is a challenging task in the field of fashion technology, in which the aim is to create a collocated set of fashion items that complement a given set of items. Pr…
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…
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…