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Found 5 papers, 2 papers with code
Variance-Aware Regret Bounds for Stochastic Contextual Dueling Bandits
Dueling bandits is a prominent framework for decision-making involving preferential feedback, a valuable feature that fits various applications involving human interaction, such as ranking, information retrieval, and recommendation systems.
Borda Regret Minimization for Generalized Linear Dueling Bandits
To attain this lower bound, we propose an explore-then-commit type algorithm for the stochastic setting, which has a nearly matching regret upper bound $\tilde{O}(d^{2/3} T^{2/3})$.
Adaptive Sampling for Heterogeneous Rank Aggregation from Noisy Pairwise Comparisons
In heterogeneous rank aggregation problems, users often exhibit various accuracy levels when comparing pairs of items.
Rank Aggregation via Heterogeneous Thurstone Preference Models
By allowing different noise distributions, the proposed HTM model maintains the generality of Thurstone's original framework, and as such, also extends the Bradley-Terry-Luce (BTL) model for pairwise comparisons to heterogeneous populations of users.
The Capacity of String-Replication Systems
It is known that the majority of the human genome consists of repeated sequences.
