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Found 5 papers, 1 papers with code
Rank Aggregation from Pairwise Comparisons in the Presence of Adversarial Corruptions
In this paper, we initiate the study of robustness in rank aggregation under the popular Bradley-Terry-Luce (BTL) model for pairwise comparisons.
Misalignment, Learning, and Ranking: Harnessing Users Limited Attention
Each user selects an item by first considering a prefix window of these ranked items and then picking the highest preferred item in that window (and the platform observes its payoff for this item).
Sublinear Algorithms for Hierarchical Clustering
At the heart of our algorithmic results is a view of the objective in terms of cuts in the graph, which allows us to use a relaxed notion of cut sparsifiers to do hierarchical clustering while introducing only a small distortion in the objective function.
A Sharp Memory-Regret Trade-Off for Multi-Pass Streaming Bandits
In this paper we study the trade-off between memory and regret when $B$ passes over the stream are allowed, for any $B \geq 1$, and establish tight regret upper and lower bounds for any $B$-pass algorithm.
Accelerated Spectral Ranking
In this paper, we design a provably faster spectral ranking algorithm, which we call accelerated spectral ranking (ASR), that is also consistent under the MNL/BTL models.
