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Xiaofeng Zhu

4 papers hereh-index 337 citations9 works total

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

author position
  • middle author2
  • last author2

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

fields
  • cs.IR2
  • cs.AI1
  • cs.CV1
same name
  • Xiaofeng Zhu — 16 papers, h 21
  • Xiaofeng Zhu — 13 papers, h 55
  • Xiaofeng Zhu — 8 papers, h 5
  • Xiaofeng Zhu — 7 papers, h 8
  • Xiaofeng Zhu — 6 papers, h 4
  • Xiaofeng Zhu — 5 papers, h 2

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 citedRevisiting Self-Supervised Heterogeneous Graph Learning from Spectral Clustering Perspective

1 citations · 1 across the 4 of their papers we have counts for

collaborators

4 papers

cs.IR2026

Auto-Bidding with Disentangled Advertiser Profiles and Train-Free Adaptation

Songyue Cai, Shan Gu, Wei Chen +4

Auto-bidding is a key component of modern advertising systems that provides a personalized bidding strategy for each advertiser. By characterizing each individual, profile-based me…

cs.IR2026

Strategy-Aware Parameter-Efficient Adaptation for LLM-based Auto-Bidding

Songyue Cai, Lianyu Wang, Shan Gu +4

Advertising bidding has evolved from manual strategies to auto-bidding systems better adapted for large-scale, dynamic auction environments. While recent advances in Large Language…

cs.CV2025

Background Prompt for Few-Shot Out-of-Distribution Detection

Songyue Cai, Zongqian Wu, Yujie Mo +4

Existing foreground-background (FG-BG) decomposition methods for the few-shot out-of-distribution (FS-OOD) detection often suffer from low robustness due to over-reliance on the lo…

cs.AI2024★ 1 cited

Revisiting Self-Supervised Heterogeneous Graph Learning from Spectral Clustering Perspective

Yujie Mo, Zhihe Lu, Runpeng Yu +2

Self-supervised heterogeneous graph learning (SHGL) has shown promising potential in diverse scenarios. However, while existing SHGL methods share a similar essential with clusteri…

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