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Xinyu Yang

13 papers hereh-index 357.1k citations182 works total

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

author position
  • middle author8
  • last author5

Across the 13 of 13 papers where every author was matched, so the position is known.

fields
  • cs.CR4
  • cs.DC3
  • cs.LG3
  • cs.GT2
  • cs.IR1
same name
  • Xinyu Yang — 21 papers, h 9
  • Xinyu Yang — 11 papers, h 18
  • Xinyu Yang — 10 papers
  • Xinyu Yang — 9 papers, h 8
  • Xinyu Yang — 7 papers, h 3
  • Xinyu Yang — 7 papers, h 3

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
20172022
most citedPreserving Dynamic Attention for Long-Term Spatial-Temporal Prediction

59 citations · 118 across the 10 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2023

Fuzziness-tuned: Improving the Transferability of Adversarial Examples

Xiangyuan Yang, Jie Lin, Hanlin Zhang +2

With the development of adversarial attacks, adversairal examples have been widely used to enhance the robustness of the training models on deep neural networks. Although considera…

cs.LG2020

Latent Dirichlet Allocation Model Training with Differential Privacy

Fangyuan Zhao, Xuebin Ren, Shusen Yang +3

Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for hidden semantic discovery of text data and serves as a fundamental tool for text analysis in various app…

cs.LG2019

On Privacy Protection of Latent Dirichlet Allocation Model Training

Fangyuan Zhao, Xuebin Ren, Shusen Yang +1

Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for discovery of hidden semantic architecture of text datasets, and plays a fundamental role in many machine…

cs.LG2019★ 28 cited

Impact of Prior Knowledge and Data Correlation on Privacy Leakage: A Unified Analysis

Yanan Li, Xuebin Ren, Shusen Yang +1

It has been widely understood that differential privacy (DP) can guarantee rigorous privacy against adversaries with arbitrary prior knowledge. However, recent studies demonstrate…

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