Efficient Second-Order Online Kernel Learning with Adaptive Embedding

Daniele Calandriello, Alessandro Lazaric, Michal Valko

Neural Information Processing Systems 

Related work Although first-order OKL methods cannot achieve logarithmic regret, many approximation methods have been proposed to make them scale to large datasets.

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