3 accepted papers
We introduce the Correlated Preference Bandits problem with random utility-based choice models (RUMs), where the goal is to identify the best item from a given pool of $n$ items through online subsetwise preference feedback. We investigate whether models with a simple correlation structure, e.g. low…
Sparsity is a basic property of real vectors that is exploited in a wide variety of machine learning applications. In this work, we describe property testing algorithms for sparsity that observe a low-dimensional projec- tion of the input. We consider two settings. In the first setting, we test spar…
We consider the problem of ranking n items from stochastically sampled pairwise preferences. It was shown recently that when the underlying pairwise preferences are acyclic, several algorithms including the Rank Centrality algorithm, the Matrix Borda algorithm, and the SVM-RankAggregation algorithm…