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In recent years rank aggregation has received significant attention from the machine learning community. The goal of such a problem is to combine the (partially revealed) preferences over objects of a large population into a single, relatively consistent ordering of those objects. However, in many cases, we might not want a single ranking and instead opt for individual rankings. We study a version...
Stochastic optimization algorithms have many desirable features for large-scale machine learning, and accordingly have been the focus of renewed and intensive study in the last several years (e.g., see the papers [2], [5], [14] and references therein). The empirical efficiency of these methods is backed with strong theoretical guarantees, providing sharp bounds on their convergence rates. These convergence...
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