A kernel SVM method for extreme quantile regression that provides finite-sample guarantees for out-of-distribution generalization under heavy-tailed inputs.
liquidSVM: A Fast and Versatile SVM package
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abstract
liquidSVM is a package written in C++ that provides SVM-type solvers for various classification and regression tasks. Because of a fully integrated hyper-parameter selection, very carefully implemented solvers, multi-threading and GPU support, and several built-in data decomposition strategies it provides unprecedented speed for small training sizes as well as for data sets of tens of millions of samples. Besides the C++ API and a command line interface, bindings to R, MATLAB, Java, Python, and Spark are available. We present a brief description of the package and report experimental comparisons to other SVM packages.
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stat.ML 1years
2026 1verdicts
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Out-of-Distribution generalization of quantile regression with heavy tailed inputs: an SVM approach
A kernel SVM method for extreme quantile regression that provides finite-sample guarantees for out-of-distribution generalization under heavy-tailed inputs.