4 papers
Variance-Aware Regret Bounds for Stochastic Contextual Dueling Bandits
Qiwei Di, Tao Jin, Yue Wu +3
Dueling bandits is a prominent framework for decision-making involving preferential feedback, a valuable feature that fits various applications involving human interaction, such as…
Borda Regret Minimization for Generalized Linear Dueling Bandits
Yue Wu, Tao Jin, Hao Lou +2
Dueling bandits are widely used to model preferential feedback prevalent in many applications such as recommendation systems and ranking. In this paper, we study the Borda regret m…
Adaptive Sampling for Heterogeneous Rank Aggregation from Noisy Pairwise Comparisons
Yue Wu, Tao Jin, Hao Lou +3
In heterogeneous rank aggregation problems, users often exhibit various accuracy levels when comparing pairs of items. Thus a uniform querying strategy over users may not be optima…
Rank Aggregation via Heterogeneous Thurstone Preference Models
Tao Jin, Pan Xu, Quanquan Gu +1
We propose the Heterogeneous Thurstone Model (HTM) for aggregating ranked data, which can take the accuracy levels of different users into account. By allowing different noise dist…