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Philip S. Yu

8 papers hereh-index 5159 citations9 works total

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

author position
  • last author7

Across the 7 of 8 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • cs.CL2
  • cs.DB1
  • cs.IR1
same name
  • Philip S. Yu — 287 papers, h 168
  • Philip S. Yu — 85 papers, h 45
  • Philip S. Yu — 29 papers, h 11
  • Philip S. Yu — 28 papers, h 10
  • Philip S. Yu — 28 papers, h 7
  • Philip S. Yu — 24 papers, h 17

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

most citedHarnessing Multiple Large Language Models: A Survey on LLM Ensemble

5 citations · 5 across the 7 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2026

NanoNet: Parameter-Efficient Learning with Label-Scarce Supervision for Lightweight Text Mining Model

Qianren Mao, Yashuo Luo, Ziqi Qin +12

The lightweight semi-supervised learning (LSL) strategy provides an effective approach of conserving labeled samples and minimizing model inference costs. Prior research has effect…

cs.LG2025

Evolving Graph Learning for Out-of-Distribution Generalization in Non-stationary Environments

Qingyun Sun, Jiayi Luo, Haonan Yuan +4

Graph neural networks have shown remarkable success in exploiting the spatial and temporal patterns on dynamic graphs. However, existing GNNs exhibit poor generalization ability un…

cs.LG2025

Robust Graph Condensation via Classification Complexity Mitigation

Jiayi Luo, Qingyun Sun, Beining Yang +5

Graph condensation (GC) has gained significant attention for its ability to synthesize smaller yet informative graphs. However, existing studies often overlook the robustness of GC…

cs.LG2025

GRAVER: Generative Graph Vocabularies for Robust Graph Foundation Models Fine-tuning

Haonan Yuan, Qingyun Sun, Junhua Shi +4

Inspired by the remarkable success of foundation models in language and vision, Graph Foundation Models (GFMs) hold significant promise for broad applicability across diverse graph…

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