Philip S. Yu

Privacy-Preserving Federated Mining of Utility-Based Patterns

Collaborative high-utility pattern mining (HUPM) aims to identify high-utility patterns (HUPs) across multiple data owners, and it is particularly suitable for distributed Internet of Things (IoT) scenarios such as smart retail and edge-assisted service analytics. However, collaborative mining raises privacy concerns,...

Multi-Modal Hypergraphs Fusion With Consistency and Complementarity for Multi-Modal Emotion Recognition in Conversations

Geometry-aware Test-Time Adaptation on Graphs

Test-time adaptation (TTA) has been a widely-studied paradigm for adapting well-trained models to distributionally shifted test data. Recent works extend TTA to graphs using graph augmentations and self-supervised objectives in Euclidean space. However, graph-structured data often exhibit heterogeneity where semantic s...

From Generic Intelligence to Personalized AI: A Tutorial on Foundations of LLM Personalization

This tutorial provides a systematic overview of LLM personalization, covering core paradigms such as user-aware representations, prompt-based adaptation, memory augmentation, parameter-efficient fine-tuning, and continual learning.

The 15th International Workshop on Urban Computing

A vision of urban computing is presented, which aims to unlock the power of knowledge from big and heterogeneous data collected in urban spaces and apply this powerful information to solve critical problems the authors' cities face today, ultimately fostering sustainable ecosystems and elevating the human experience.

Artificial intelligence for sleep-disordered breathing: a taxonomy of methods, data modalities, and clinical applications

Sleep-disordered breathing (SDB), particularly obstructive sleep apnea (OSA), is a highly prevalent group of disorders associated with disrupted sleep, intermittent hypoxemia, excessive daytime sleepiness, neurocognitive impairment, and cardiometabolic morbidity. Although laboratory polysomnography remains central to d...

The 5th Workshop on AI Agent for Information Retrieval: Generating and Ranking

This workshop aims to bring together researchers and practitioners to discuss recent advances, practical applications, and future directions of AI agents in information retrieval, while encouraging collaboration and knowledge exchange within the community.

2nd Workshop on Frontiers in Graph Machine Learning for the Large Model Era

This workshop explores how graph learning can support emerging challenges in knowledge reasoning, temporal and multi-hop inference, and AI systems and investigates how advances in representation learning, structure-aware generalization, and efficient graph processing can contribute to trustworthy and scalable AI system...