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researcher

Qing Cui

4 papers hereh-index 4106 citations6 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author3

Across the 3 of 4 papers where every author was matched, so the position is known.

fields
  • cs.LG2
  • cs.CL1
  • cs.IR1
same name
  • Qing Cui — 17 papers, h 5
  • Qing Cui — 4 papers, h 11
  • Qing Cui — 4 papers, h 2
  • Qing Cui — 3 papers, h 8
  • Qing Cui — 2 papers, h 2
  • Qing Cui — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20242026
most citedLLM-Guided Multi-View Hypergraph Learning for Human-Centric Explainable Recommendation

2 citations · 3 across the 4 of their papers we have counts for

collaborators

4 papers

cs.CL2026

Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scale

Ang Li, Ben Liu, Bin Han +215

Efficient and scalable agentic intelligence requires models that can deliver both low-latency responses and strong reasoning capabilities while remaining practical to train, serve,…

cs.LG2025

A Unified Invariant Learning Framework for Graph Classification

Yongduo Sui, Jie Sun, Shuyao Wang +4

Invariant learning demonstrates substantial potential for enhancing the generalization of graph neural networks (GNNs) with out-of-distribution (OOD) data. It aims to recognize sta…

cs.LG2024★ 1 cited

Task-Driven Causal Feature Distillation: Towards Trustworthy Risk Prediction

Zhixuan Chu, Mengxuan Hu, Qing Cui +2

Since artificial intelligence has seen tremendous recent successes in many areas, it has sparked great interest in its potential for trustworthy and interpretable risk prediction.…

cs.IR2024★ 2 cited

LLM-Guided Multi-View Hypergraph Learning for Human-Centric Explainable Recommendation

Zhixuan Chu, Yan Wang, Qing Cui +4

As personalized recommendation systems become vital in the age of information overload, traditional methods relying solely on historical user interactions often fail to fully captu…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.