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Xing Tang

16 papers hereh-index 680 citations18 works total

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

author position
  • first author2
  • middle author13

Across the 15 of 16 papers where every author was matched, so the position is known.

fields
  • cs.IR9
  • cs.LG5
  • cs.CL2
same name
  • Xing Tang — 16 papers, h 10
  • Xing Tang — 13 papers, h 7
  • Xing Tang — 4 papers, h 4
  • Xing Tang — 3 papers, h 1
  • Xing Tang — 3 papers, h 2
  • Xing Tang — 3 papers, h 6

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 citedMasked Random Noise for Communication Efficient Federated Learning

7 citations · 8 across the 16 of their papers we have counts for

collaborators
Showing 2024Show all

4 papers · 1 filter

cs.IR2024★ 1 cited

Mixed-Precision Embeddings for Large-Scale Recommendation Models

Shiwei Li, Zhuoqi Hu, Xing Tang +6

Embedding techniques have become essential components of large databases in the deep learning era. By encoding discrete entities, such as words, items, or graph nodes, into continu…

cs.LG2024★ 7 cited

Masked Random Noise for Communication Efficient Federated Learning

Shiwei Li, Yingyi Cheng, Haozhao Wang +7

Federated learning is a promising distributed training paradigm that effectively safeguards data privacy. However, it may involve significant communication costs, which hinders tra…

cs.IR2024

Efficient Transfer Learning Framework for Cross-Domain Click-Through Rate Prediction

Qi Liu, Xingyuan Tang, Jianqiang Huang +9

Natural content and advertisement coexist in industrial recommendation systems but differ in data distribution. Concretely, traffic related to the advertisement is considerably spa…

cs.LG2024

FedBAT: Communication-Efficient Federated Learning via Learnable Binarization

Shiwei Li, Wenchao Xu, Haozhao Wang +7

Federated learning is a promising distributed machine learning paradigm that can effectively exploit large-scale data without exposing users' privacy. However, it may incur signifi…

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