Online learning represents a family of machine learning methods, where a learner attempts
to tackle some predictive (or any type of decision-making) task by learning from a sequence …

A sequence of works in unconstrained online convex optimisation have investigated the
possibility of adapting simultaneously to the norm U of the comparator and the maximum …

In this paper, we tackle the computational efficiency of kernelized UCB algorithms in
contextual bandits. While standard methods require a $\mathcal {O}(CT^ 3) $ complexity …

Mixability has been shown to be a powerful tool to obtain algorithms with optimal regret.
However, the resulting methods often suffer from high computational complexity which has …

We propose and analyze a kernelized version of Q-learning. Although a kernel space is
typically infinite-dimensional, extensive study has shown that generalization is only affected …

We consider the setting of online logistic regression and consider the regret with respect to
the $\ell_2 $-ball of radius $ B $. It is known (see Hazan et al.(2014)) that any proper …

We study the problem of online kernel selection under computational constraints, where the
memory or time of kernel selection and online prediction procedures is restricted to a fixed …

Online forecasting under a changing environment has been a problem of increasing
importance in many real-world applications. In this paper, we consider the meta-algorithm …

The trade-off between regret and computational cost is a fundamental problem for online
kernel regression, and previous algorithms worked on the trade-off can not keep optimal …

We consider the framework of non-stationary Online Convex Optimization where a learner
seeks to control its dynamic regret against an arbitrary sequence of comparators. When the …