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Found 2 papers, 1 papers with code
Sketch In, Sketch Out: Accelerating both Learning and Inference for Structured Prediction with Kernels
Surrogate kernel-based methods offer a flexible solution to structured output prediction by leveraging the kernel trick in both input and output spaces.
Fast Kernel Methods for Generic Lipschitz Losses via $p$-Sparsified Sketches
Kernel methods are learning algorithms that enjoy solid theoretical foundations while suffering from important computational limitations.
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