22 citations · 65 across the 22 of their papers we have counts for
22 papers
Delayed Commitment for Representation Readiness in Stage-wise Audio-Visual Learning
Xinmeng Xu, Haoran Xie, S. Joe Qin +3
Stage-wise audio-visual encoders propagate fused intermediate states across layers, making the formation of later representations depend on the readiness of earlier fusion states.…
Concept than Document: Context Compression via AMR-based Conceptual Entropy
Kaize Shi, Xueyao Sun, Xiaohui Tao +3
Large Language Models (LLMs) face information overload when handling long contexts, particularly in Retrieval-Augmented Generation (RAG) where extensive supporting documents often…
Signed Graph Unlearning
Zhifei Luo, Lin Li, Xiaohui Tao +1
The proliferation of signed networks in contemporary social media platforms necessitates robust privacy-preserving mechanisms. Graph unlearning, which aims to eliminate the influen…
Factor Decorrelation Enhanced Data Removal from Deep Predictive Models
Wenhao Yang, Lin Li, Xiaohui Tao +1
The imperative of user privacy protection and regulatory compliance necessitates sensitive data removal in model training, yet this process often induces distributional shifts that…
HiFIRec Towards High-Frequency yet Low-Intention Behaviors for Multi-Behavior Recommendation
Ruiqi Luo, Ran Jin, Kaixi Hu +2
Multi behavior recommendation leverages multiple types of user-item interactions to address data sparsity and cold-start issues,providing personalized services in domains such as h…
A Node-Aware Dynamic Quantization Approach for Graph Collaborative Filtering
Lin Li, Chunyang Li, Yu Yin +2
In the realm of collaborative filtering recommendation systems, Graph Neural Networks (GNNs) have demonstrated remarkable performance but face significant challenges in deployment…
Modeling Item-Level Dynamic Variability with Residual Diffusion for Bundle Recommendation
Dong Zhang, Lin Li, Ming Li +4
Existing solutions for bundle recommendation (BR) have achieved remarkable effectiveness for predicting the user's preference for prebuilt bundles. However, bundle-item (B-I) affil…
Exploring Incremental Unlearning: Techniques, Challenges, and Future Directions
Sadia Qureshi, Thanveer Shaik, Xiaohui Tao +4
The growing demand for data privacy in Machine Learning (ML) applications has seen Machine Unlearning (MU) emerge as a critical area of research. As the `right to be forgotten' bec…
A Survey on Bundle Recommendation: Methods, Applications, and Challenges
Meng Sun, Lin Li, Ming Li +5
In recent years, bundle recommendation systems have gained significant attention in both academia and industry due to their ability to enhance user experience and increase sales by…
MealRec: A Meal Recommendation Dataset with Meal-Course Affiliation for Personalization and Healthiness
Ming Li, Lin Li, Xiaohui Tao +1
Meal recommendation, as a typical health-related recommendation task, contains complex relationships between users, courses, and meals. Among them, meal-course affiliation associat…
CrimeAlarm: Towards Intensive Intent Dynamics in Fine-grained Crime Prediction
Kaixi Hu, Lin Li, Qing Xie +2
Granularity and accuracy are two crucial factors for crime event prediction. Within fine-grained event classification, multiple criminal intents may alternately exhibit in precedin…
Primary and Secondary Factor Consistency as Domain Knowledge to Guide Happiness Computing in Online Assessment
Xiaohua Wu, Lin Li, Xiaohui Tao +2
Happiness computing based on large-scale online web data and machine learning methods is an emerging research topic that underpins a range of issues, from personal growth to social…
Towards Causal Classification: A Comprehensive Study on Graph Neural Networks
Simi Job, Xiaohui Tao, Taotao Cai +3
The exploration of Graph Neural Networks (GNNs) for processing graph-structured data has expanded, particularly their potential for causal analysis due to their universal approxima…
Persona-centric Metamorphic Relation guided Robustness Evaluation for Multi-turn Dialogue Modelling
Yanbing Chen, Lin Li, Xiaohui Tao +1
Recently there has been significant progress in the field of dialogue system thanks to the introduction of training paradigms such as fine-tune and prompt learning. Persona can fun…
Exploring Causal Learning through Graph Neural Networks: An In-depth Review
Simi Job, Xiaohui Tao, Taotao Cai +4
In machine learning, exploring data correlations to predict outcomes is a fundamental task. Recognizing causal relationships embedded within data is pivotal for a comprehensive und…
Adaptive Multi-Agent Deep Reinforcement Learning for Timely Healthcare Interventions
Thanveer Shaik, Xiaohui Tao, Lin Li +4
Effective patient monitoring is vital for timely interventions and improved healthcare outcomes. Traditional monitoring systems often struggle to handle complex, dynamic environmen…
QXAI: Explainable AI Framework for Quantitative Analysis in Patient Monitoring Systems
Thanveer Shaik, Xiaohui Tao, Haoran Xie +3
Artificial Intelligence techniques can be used to classify a patient's physical activities and predict vital signs for remote patient monitoring. Regression analysis based on non-l…
FRAMU: Attention-based Machine Unlearning using Federated Reinforcement Learning
Thanveer Shaik, Xiaohui Tao, Lin Li +4
Machine Unlearning is an emerging field that addresses data privacy issues by enabling the removal of private or irrelevant data from the Machine Learning process. Challenges relat…
Graph-enabled Reinforcement Learning for Time Series Forecasting with Adaptive Intelligence
Thanveer Shaik, Xiaohui Tao, Haoran Xie +3
Reinforcement learning is well known for its ability to model sequential tasks and learn latent data patterns adaptively. Deep learning models have been widely explored and adopted…
Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy
Thanveer Shaik, Xiaohui Tao, Haoran Xie +3
Machine unlearning (MU) is gaining increasing attention due to the need to remove or modify predictions made by machine learning (ML) models. While training models have become more…
MealRec: A Meal Recommendation Dataset
Ming Li, Lin Li, Qing Xie +2
Bundle recommendation systems aim to recommend a bundle of items for a user to consider as a whole. They have become a norm in modern life and have been applied to many real-world…
What is Next when Sequential Prediction Meets Implicitly Hard Interaction?
Kaixi Hu, Lin Li, Qing Xie +2
Hard interaction learning between source sequences and their next targets is challenging, which exists in a myriad of sequential prediction tasks. During the training process, most…