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Optimistic bounds for multi-output prediction
We investigate the challenge of multi-output learning, where the goal is to learn a vector-valued function based on a supervised data set.
Minimax rates for cost-sensitive learning on manifolds with approximate nearest neighbours
We study the approximate nearest neighbour method for cost-sensitive classification on low-dimensional manifolds embedded within a high-dimensional feature space.
Diversity and degrees of freedom in regression ensembles
We provide an exact formula for the effective degrees of freedom in an NCL ensemble with fixed basis functions, showing that it is a continuous, convex and monotonically increasing function of the diversity parameter.
The K-Nearest Neighbour UCB algorithm for multi-armed bandits with covariates
In addition, focusing on the case of bounded rewards, we give corresponding regret bounds for the k-Nearest Neighbour KL-UCB algorithm, which is an analogue of the KL-UCB algorithm adapted to the setting of multi-armed bandits with covariates.
