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Poster
ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitions
Luigi Carratino · Stefano Vigogna · Daniele Calandriello · Lorenzo Rosasco

Fri Dec 10 08:30 AM -- 10:00 AM (PST) @
in Poster Session 8 »

We introduce ParK, a new large-scale solver for kernel ridge regression. Our approach combines partitioning with random projections and iterative optimization to reduce space and time complexity while provably maintaining the same statistical accuracy. In particular, constructing suitable partitions directly in the feature space rather than in the input space, we promote orthogonality between the local estimators, thus ensuring that key quantities such as local effective dimension and bias remain under control. We characterize the statistical-computational tradeoff of our model, and demonstrate the effectiveness of our method by numerical experiments on large-scale datasets.

Keywords: Optimization · Kernel Methods

Author Information

Luigi Carratino (Zalando)
Stefano Vigogna (University of Genova)
Daniele Calandriello (DeepMind)
Lorenzo Rosasco (Università degli Studi di Genova)

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