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@article{Pfeuffer2016ABP, title={A Bounded p-norm Approximation of Max-Convolution for Sub-Quadratic Bayesian Inference on Additive Factors}, author={Julianus Pfeuffer and Oliver Serang}, journal={Journal of Machine Learning Research}, year={2016}, volume={17}, pages={36:1-36:39} }

- Published in Journal of Machine Learning Research 2016

Max-convolution is an important problem closely resembling standard convolution; as such, max-convolution occurs frequently across many fields. Here we extend the method with fastest known worst-case runtime, which can be applied to nonnegative vectors by numerically approximating the Chebyshev norm ‖ · ‖∞, and use this approach to derive two numerically stable methods based on the idea of computing pnorms via fast convolution: The first method proposed, with runtime in O(k log(k) log(log(k… CONTINUE READING

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