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Sketch In, Sketch Out: Accelerating both Learning and Inference for Structured Prediction with Kernels
May 7, 2024, 4:45 a.m. | Tamim El Ahmad, Luc Brogat-Motte, Pierre Laforgue, Florence d'Alch\'e-Buc
cs.LG updates on arXiv.org arxiv.org
Abstract: Leveraging the kernel trick in both the input and output spaces, surrogate kernel methods are a flexible and theoretically grounded solution to structured output prediction. If they provide state-of-the-art performance on complex data sets of moderate size (e.g., in chemoinformatics), these approaches however fail to scale. We propose to equip surrogate kernel methods with sketching-based approximations, applied to both the input and output feature maps. We prove excess risk bounds on the original structured prediction …
abstract art arxiv cs.lg data data sets inference kernel performance prediction solution spaces state stat.ml trick type
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