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Creators/Authors contains: "Liu,  Lang"

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  1. ; ; ; ; (, Proceedings of The 26th International Conference on Artificial Intelligence and Statistics)
    Ruiz, Francisco; Dy, Jennifer; van de Meent, Jan-Willem (Ed.)
    Influence diagnostics such as influence functions and approximate maximum influence perturbations are popular in machine learning and in AI domain applications. Influence diagnostics are powerful statistical tools to identify influential datapoints or subsets of datapoints. We establish finite-sample statistical bounds, as well as computational complexity bounds, for influence functions and approximate maximum influence perturbations using efficient inverse-Hessian-vector product implementations. We illustrate our results with generalized linear models and large attention based models on synthetic and real data. 
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    Accepted Manuscript Full Text Available
  2. ; ; ; ; (, 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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    Accepted Manuscript Full Text Available