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stat.ML2018
Adaptive Minimax Regret against Smooth Logarithmic Losses over High-Dimensional -Balls via Envelope Complexity
Kohei Miyaguchi, Kenji Yamanishi
We develop a new theoretical framework, the \emph{envelope complexity}, to analyze the minimax regret with logarithmic loss functions and derive a Bayesian predictor that adaptivel…
stat.ML2018★ 2 cited
High-dimensional Penalty Selection via Minimum Description Length Principle
Kohei Miyaguchi, Kenji Yamanishi
We tackle the problem of penalty selection of regularization on the basis of the minimum description length (MDL) principle. In particular, we consider that the design space of the…