63 citations · 67 across the 11 of their papers we have counts for
11 papers
Trading Off Voting Axioms for Privacy
Zhechen Li, Ao Liu, Lirong Xia +2
In this paper, we investigate tradeoffs among differential privacy (DP) and several important voting axioms: Pareto efficiency, SD-efficiency, PC-efficiency, Condorcet consistency,…
Accelerating Voting by Quantum Computation
Ao Liu, Qishen Han, Lirong Xia +1
Studying the computational complexity and designing fast algorithms for determining winners under voting rules are classical and fundamental questions in computational social choic…
Differentially Private Condorcet Voting
Zhechen Li, Ao Liu, Lirong Xia +2
Designing private voting rules is an important and pressing problem for trustworthy democracy. In this paper, under the framework of differential privacy, we propose a novel famliy…
Truthful Information Elicitation from Hybrid Crowds
Qishen Han, Sikai Ruan, Yuqing Kong +3
Suppose a decision maker wants to predict weather tomorrow by eliciting and aggregating information from crowd. How can the decision maker incentivize the crowds to report their in…
Certifiably Robust Interpretation via Renyi Differential Privacy
Ao Liu, Xiaoyu Chen, Sijia Liu +2
Motivated by the recent discovery that the interpretation maps of CNNs could easily be manipulated by adversarial attacks against network interpretability, we study the problem of…
Smoothed Differential Privacy
Ao Liu, Yu-Xiang Wang, Lirong Xia
Differential privacy (DP) is a widely-accepted and widely-applied notion of privacy based on worst-case analysis. Often, DP classifies most mechanisms without additive noise as non…
The Smoothed Likelihood of Doctrinal Paradox
Ao Liu, Lirong Xia
When aggregating logically interconnected judgments from agents, the result might be inconsistent with the logical connection. This inconsistency is known as the doctrinal para…
Learning Mixtures of Random Utility Models with Features from Incomplete Preferences
Zhibing Zhao, Ao Liu, Lirong Xia
Random Utility Models (RUMs), which subsume Plackett-Luce model (PL) as a special case, are among the most popular models for preference learning. In this paper, we consider RUMs w…
Differential Privacy for Eye-Tracking Data
Ao Liu, Lirong Xia, Andrew Duchowski +3
As large eye-tracking datasets are created, data privacy is a pressing concern for the eye-tracking community. De-identifying data does not guarantee privacy because multiple datas…
Towards Non-Parametric Learning to Rank
Ao Liu, Qiong Wu, Zhenming Liu +1
This paper studies a stylized, yet natural, learning-to-rank problem and points out the critical incorrectness of a widely used nearest neighbor algorithm. We consider a model with…
How Private Are Commonly-Used Voting Rules?
Ao Liu, Yun Lu, Lirong Xia +1
Differential privacy has been widely applied to provide privacy guarantees by adding random noise to the function output. However, it inevitably fails in many high-stakes voting sc…