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Xiaoying Zhang

7 papers hereh-index 9797 citations17 works total

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

author position
  • first author3
  • middle author4

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

fields
  • cs.LG4
  • cs.AI1
  • cs.CR1
  • cs.IR1
same name
  • Xiaoying Zhang — 7 papers, h 10
  • Xiaoying Zhang — 7 papers, h 5
  • Xiaoying Zhang — 6 papers, h 5
  • Xiaoying Zhang — 4 papers, h 3
  • Xiaoying Zhang — 3 papers, h 1
  • Xiaoying Zhang — 2 papers, h 5

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
20192026
most citedTrustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment

73 citations · 86 across the 7 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2024★ 6 cited

Conversational Dueling Bandits in Generalized Linear Models

Shuhua Yang, Hui Yuan, Xiaoying Zhang +3

Conversational recommendation systems elicit user preferences by interacting with users to obtain their feedback on recommended commodities. Such systems utilize a multi-armed band…

cs.LG2023

Uncertainty-Aware Instance Reweighting for Off-Policy Learning

Xiaoying Zhang, Junpu Chen, Hongning Wang +4

Off-policy learning, referring to the procedure of policy optimization with access only to logged feedback data, has shown importance in various real-world applications, such as se…

cs.LG2023★ 2 cited

Debiasing Recommendation by Learning Identifiable Latent Confounders

Qing Zhang, Xiaoying Zhang, Yang Liu +4

Recommendation systems aim to predict users' feedback on items not exposed to them. Confounding bias arises due to the presence of unmeasured variables (e.g., the socio-economic st…

cs.LG2019★ 5 cited

Conversational Contextual Bandit: Algorithm and Application

Xiaoying Zhang, Hong Xie, Hang Li +1

Contextual bandit algorithms provide principled online learning solutions to balance the exploitation-exploration trade-off in various applications such as recommender systems. How…

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