• Corpus ID: 11379717

Second-Order Kernel Online Convex Optimization with Adaptive Sketching

@inproceedings{Calandriello2017SecondOrderKO,
  title={Second-Order Kernel Online Convex Optimization with Adaptive Sketching},
  author={Daniele Calandriello and Alessandro Lazaric and Michal Valko},
  booktitle={International Conference on Machine Learning},
  year={2017}
}
Kernel online convex optimization (KOCO) is a framework combining the expressiveness of non-parametric kernel models with the regret guarantees of online learning. First-order KOCO methods such as functional gradient descent require only $O(t)$ time and space per iteration, and, when the only information on the losses is their convexity, achieve a minimax optimal $O(\sqrt{T})$ regret. Nonetheless, many common losses in kernel problems, such as squared loss, logistic loss, and squared hinge loss… 

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