activity
20232025
most citedInvariant Collaborative Filtering to Popularity Distribution Shift

50 citations · 72 across the 9 of their papers we have counts for

collaborators

8 papers

math.OC2025

PyClustrPath: An efficient Python package for generating clustering paths with GPU acceleration

Hongfei Wu, Yancheng Yuan

Convex clustering is a popular clustering model without requiring the number of clusters as prior knowledge. It can generate a clustering path by continuously solving the model wit…

cs.LG2024

Adaptive Self-supervised Robust Clustering for Unstructured Data with Unknown Cluster Number

Chen-Lu Ding, Jiancan Wu, Wei Lin +3

We introduce a novel self-supervised deep clustering approach tailored for unstructured data without requiring prior knowledge of the number of clusters, termed Adaptive Self-super…

cs.CR2024

Collective Certified Robustness against Graph Injection Attacks

Yuni Lai, Bailin Pan, Kaihuang Chen +2

We investigate certified robustness for GNNs under graph injection attacks. Existing research only provides sample-wise certificates by verifying each node independently, leading t…

cs.LG20231 cited

XAI for In-hospital Mortality Prediction via Multimodal ICU Data

Xingqiao Li, Jindong Gu, Zhiyong Wang +3

Predicting in-hospital mortality for intensive care unit (ICU) patients is key to final clinical outcomes. AI has shown advantaged accuracy but suffers from the lack of explainabil…

cs.IR20236 cited

Large Language Model Can Interpret Latent Space of Sequential Recommender

Zhengyi Yang, Jiancan Wu, Yanchen Luo +5

Sequential recommendation is to predict the next item of interest for a user, based on her/his interaction history with previous items. In conventional sequential recommenders, a c…

cs.IR202315 cited

Generate What You Prefer: Reshaping Sequential Recommendation via Guided Diffusion

Zhengyi Yang, Jiancan Wu, Zhicai Wang +3

Sequential recommendation aims to recommend the next item that matches a user's interest, based on the sequence of items he/she interacted with before. Scrutinizing previous studie…