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researcher

Xiangnan He

45 papers hereh-index 201.7k citations58 works total

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

author position
  • middle author14
  • last author29

Across the 43 of 45 papers where every author was matched, so the position is known.

fields
  • cs.IR14
  • cs.LG13
  • cs.CV8
  • cs.CL6
  • cs.AI4
same name
  • Xiangnan He — 143 papers, h 102
  • Xiangnan He — 46 papers, h 18
  • Xiangnan He — 7 papers, h 6
  • Xiangnan He — 3 papers, h 5
  • Xiangnan He — 1 paper, h 4
  • Xiangnan He — 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
20182026
most citedExplainable Reasoning over Knowledge Graphs for Recommendation

80 citations · 211 across the 42 of their papers we have counts for

collaborators
Showing cs.AIShow all

4 papers · 1 filter

cs.AI2026

CUSE: Fine-Grained Multimodal Unlearning with Counterfactual Guidance and On-Policy Self-Distillation

Chenlu Ding, Jiancan Wu, Yanchen Luo +4

Multimodal large language models (MLLMs) can reproduce sensitive, copyrighted, or unsafe information from their training data. Effective unlearning must remove target information w…

cs.AI2026

RePolicy: Reinforcement Learning for Safety-Policy Invocation in Agent Safeguards

Houcheng Jiang, Boxuan Zhang, Qiyong Zhong +3

Safeguarding language model agents requires assessing complete execution trajectories under context-dependent safety policies. Existing policy-aware safeguards mainly rely on promp…

cs.AI2024

Unified Parameter-Efficient Unlearning for LLMs

Chenlu Ding, Jiancan Wu, Yancheng Yuan +5

The advent of Large Language Models (LLMs) has revolutionized natural language processing, enabling advanced understanding and reasoning capabilities across a variety of tasks. Fin…

cs.AI2024★ 1 cited

β-DPO: Direct Preference Optimization with Dynamic β

Junkang Wu, Yuexiang Xie, Zhengyi Yang +5

Direct Preference Optimization (DPO) has emerged as a compelling approach for training Large Language Models (LLMs) to adhere to human preferences. However, the performance of DPO…

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