73 citations · 86 across the 7 of their papers we have counts for
7 papers
Computing Maximal Per-Record Leakage and Leakage-Distortion Functions for Privacy Mechanisms under Entropy-Constrained Adversaries
Genqiang Wu, Xiaoying Zhang, Yu Qi +3
The exponential growth of data collection necessitates robust privacy protections that preserve data utility. We address information disclosure against adversaries with bounded pri…
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…
Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment
Yang Liu, Yuanshun Yao, Jean-Francois Ton +6
Ensuring alignment, which refers to making models behave in accordance with human intentions [1,2], has become a critical task before deploying large language models (LLMs) in real…
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…
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…
Disentangled Representation for Diversified Recommendations
Xiaoying Zhang, Hongning Wang, Hang Li
Accuracy and diversity have long been considered to be two conflicting goals for recommendations. We point out, however, that as the diversity is typically measured by certain pre-…
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…