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Editors contains: "an de Meent, Jan-Willem"

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  1. ; ; ; ; (, Proceedings of The 26th International Conference on Artificial Intelligence and Statistics)
    Ruiz, Francisco; Dy, Jennifer; an de Meent, Jan-Willem (Ed.)
    Spectral risk objectives – also called L-risks – allow for learning systems to interpolate between optimizing average-case performance (as in empirical risk minimization) and worst-case performance on a task. We develop LSVRG, a stochastic algorithm to optimize these quantities by characterizing their subdifferential and addressing challenges such as biasedness of subgradient estimates and non-smoothness of the objective. We show theoretically and experimentally that out-of-the-box approaches such as stochastic subgradient and dual averaging can be hindered by bias, whereas our approach exhibits linear convergence. 
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